Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2023
Abstract
Soil health assessments that integrate physical, chemical and biological indicators help the evaluation of soil functioning, provide a framework for monitoring soil degradation, guide land management activities and secure the delivery of soil ecosystem services. In this study, we assessed soil health by soil texture class on arable land in Southeast Norway and mid-Norway and between grassland and arable land in mid-Norway. We used descriptive statistics and the Welch t-test with unequal variance and Bonferroni corrections to compare a physical soil indicator (bulk density) and chemical indicators (organic matter, P-AL, K-AL, Ca-AL, Mg-AL, Na-AL and pH). We developed scoring curves from cumulative normal distribution functions on regional soil data for various soil indicators where climate, soil texture class and land use were considered. Our results show that for certain soil texture classes, average soil indicator values differed between pedo-climatic zones on arable land, but for others the difference was not significant. The variability between the pedo-climatic zones for these can be neglected, but for the ones that differ, the variability is important to consider when assessing soil health. Similarly, this was the case when comparing land use (grassland and arable land) for most soil indicators in mid-Norway. This finding illustrates the importance of addressing unique local conditions in soil health assessments. We propose aggregating similar soil texture classes where no differences are apparent when developing scoring curves. The sub-optimal levels of plant available nutrients (P-AL and K-AL) found in the soil in both pedo-climatic zones highlights the importance of suitable threshold values for targeted soil ecosystem services to ensure soil health and sustainable agricultural production. We also recommend prioritizing the most relevant soil ecosystem services to limit the number of soil indicators that need monitoring.
Abstract
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.
Abstract
Weed harrowing is commonly used to manage weeds in organic farming but is also applied in conventional farming to replace herbicides. Due to its whole-field application, weed harrowing after crop emergence has relatively poor selectivity and may cause crop damage. Weediness generally varies within a field. Therefore, there is a potential to improve the selectivity and consider the within-field variation in weediness. This paper describes a decision model for precision post-emergence weed harrowing in cereals based on experimental data in spring barley and nonlinear regression analysis. The model predicts the optimal weed harrowing intensity in terms of the tine angle of the harrow for a given weediness (in terms of percentage weed cover), a given draft force of tines, and the biological weed damage threshold (in terms of percentage weed cover). Weed cover was measured with near-ground RGB images analyzed with a machine vision algorithm based on deep learning techniques. The draft force of tines was estimated with an electronic load cell. The proposed model is the first that uses a weed damage threshold in addition to site-specific values of weed cover and soil hardness to predict the site-specific optimal weed harrow tine angle. Future field trials should validate the suggested model.
Authors
Kushan D. Siriwardhana Dimantha I. Jayaneththi Ruchiru D. Herath Randika K. Makumbura Hemantha Jayasinghe Miyuru Gunathilake Hazi Md. Azamathulla Kiran Tota-Maharaj Upaka RathnayakeAbstract
No abstract has been registered
Authors
Corine Alexis Faehn Michael Reichelt Axel Mithöfer Timo Hytönen Jørgen A.B. Mølmann Laura Elina JaakolaAbstract
Background Though many abiotic factors are constantly changing, the photoperiod is a predictable factor that enables plants to time many physiological responses. This timing is regulated by the circadian clock, yet little is known about how the clock adapts to the differences in photoperiod between mid-latitudes and high latitudes. The primary objective of this study was to compare how clock gene expression is modified in four woodland strawberry (Fragaria vesca L.) accessions originating from two different populations in Italy (IT1: Tenno, Italy, 45°N, IT4: Salorno, Italy, 46°N) and two in Northern Norway (NOR2: Alta, Norway, 69°N, NOR13: Indre Nordnes, Norway 69°N) when grown under simulated daylength conditions of an Arctic or mid-latitude photoperiod. The second objective was to investigate whether population origin or the difference in photoperiod influenced phytohormone accumulation. Results The Arctic photoperiod induced lower expression in IT4 and NOR13 for six clock genes (FvLHY, FvRVE8, FvPRR9, FvPRR7, FvPRR5, and FvLUX), in IT1 for three genes (FvLHY, FvPRR9, and FvPRR5) and in NOR2 for one gene (FvPRR9). Free-running rhythms for FvLHY in IT1 and IT4 were higher after the Arctic photoperiod, while the free-running rhythm for FvLUX in IT4 was higher after the mid-latitude photoperiod. IT1 showed significantly higher expression of FvLHY and FvPRR9 than all other accessions, as well as significantly higher expression of the circadian regulated phytohormone, abscisic acid (ABA), but low levels of salicylic acid (SA). NOR13 had significantly higher expression of FvRVE8, FvTOC1, and FvLUX than all other accessions. NOR2 had extremely low levels of auxin (IAA) and high levels of the jasmonate catabolite, hydroxyjasmonic acid (OH-JA). Conclusions Our study shows that circadian rhythms in Fragaria vesca are driven by both the experienced photoperiod and genetic factors, while phytohormone levels are primarily determined by specific accessions’ genetic factors rather than the experienced photoperiod. Keywords Arctic, Mid-latitude, Photoperiod, Daylength, Circadian clock, Phytohormones, Circadian rhythm, Fragaria vesca, Plant adaptation
Authors
Habtamu AlemAbstract
It is critical to analyze the performance of enterprises to achieve sustainable agricultural development. Several studies have been conducted to assess farm performance. However, the studies have been criticized for failing to account for farm heterogeneity (which is frequently unobserved) in their evaluation of Norwegian agricultural performance. Technically, a farm is efficient if it can produce a certain amount of output with the fewest possible inputs and no input waste. In this paper, efficiency scores are calculated using a production function with both a random intercept and a random slope parameter, addressing the issue of unobserved heterogeneity in stochastic frontier analysis. Using Norwegian dairy and crop farms as a case study, we demonstrate the viability of improving the agriculture industry and reducing resource waste. The case study was established on data collected from 5884 dairy farms and 1880 crop farms from the years 2000 to 2019. According to the empirical findings of the case study, dairy and crop producers used inefficient technologies and squandered production resources. If all farmers follow a sustainable and efficient path to produce agricultural output, they could increase output by 15–18%. Farmers must follow sustainable paths, and politicians must encourage farm experience exchange so that less efficient dairy and crop-producing farms can learn from the most efficient farms to achieve sustainable development.
Abstract
The aim of this study was to analyze the accuracy of predictions of dominant height, mean height, basal area, and volume from the nationwide forest attribute map (SR16). The analysis took advantage of field observations from 33 different forest inventory projects across Norway used for validation. Forest attributes for more than 5000 plots were predicted using non-stratified and stratified models of SR16 and the predictions were compared against corresponding ground reference values. Finally, the effect of different factors that might have influenced the prediction errors were analyzed using partial least squared regression (PLSR) to determine under which conditions the SR16 is less apt. The overall results across all plots were adequate (RMSE of 10%, MD of 2% for dominant and mean height; RMSE of 28%, MD of 4% for basal area; RMSE of 31%, MD of 5% for volume). However, when the accuracy was assessed locally for each inventory project, large differences in accuracy were observed. The MD% values for some inventory projects were substantial (>30% for basal area and volume). The results showed that stratification did not necessarily improve the results and that factors related to the forest structure had the greatest impact on the PLSR analysis.
Abstract
No abstract has been registered
Authors
Siri Gulaker Mathisen Ove Wolfgang Ville Olkkonen Érika Mata Kristina Haaskjold Ignacio Sevillano Viktoria Martin Marianne Zeyringer Dilip Khatiwada Julia HanssonAbstract
No abstract has been registered
Abstract
No abstract has been registered