Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2021
Forfattere
Anne Kjersti BakkenSammendrag
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Forfattere
Grete H. M. JørgensenSammendrag
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Forfattere
Matthias KoeslingSammendrag
Dagens temaer: Arealbruk Bruk av energi Lønnsomhet/økonomi Livsløpsanalyse, LCA Klimagassutslipp (GWP, GWP* og GTP) Økologisere driften?
Forfattere
Xinbing Wang Yuxin Miao Rui Dong Hainie Zha Tingting Xia Zhichao Chen Krzysztof Kusnierek Guohua Mi Hong Sun Minzan LiSammendrag
Reliable and efficient in-season nitrogen (N) status diagnosis and recommendation methods are crucially important for the success of crop precision N management (PNM). The accuracy of these methods has been found to be influenced by soil properties, weather conditions, and crop management practices. It is important to effectively incorporate these variables to improve in-season N management. Machine learning (ML) methods are promising due to their capability of processing different types of data and modeling both linear and non-linear relationships. The objectives of this study were to (1) determine the potential improvement of in-season prediction of corn N nutrition index (NNI) and grain yield by combining soil, weather and management data with active sensor data using random forest regression (RFR) as compared with Lasso linear regression (LR) using similar data and simple regression (SR) models only using crop sensor data; and (2) to develop a new in-season side-dress N fertilizer recommendation strategy at eighth to ninth leaf stage (V8-V9) of corn developement using the RFR model. Twelve site-year experiments examining corn N rates and planting densities were conducted in Northeast China. The GreenSeeker sensor data and corn NNI were collected at V8-V9 stage, and grain yield was determined at the harvest stage (R6). The soil information was obtained at planting and the weather data was measured throughout the growing season. The results indicated that corn NNI and grain yield were better predicted by combining soil, weather and management information with GreenSeeker sensor data using RFR model (R2 = 0.86 and 0.79) and LR model (R2 = 0.85 and 0.76) as compared with only using GreenSeeker sensor data (R2 = 0.66 and 0.62–63) based on the test dataset. An innovative in-season side-dress N recommendation strategy was developed using the RFR grain yield prediction model to simulate corn grain yield responses to a series of side-dress N rates at V8-V9 stage. Based on these response curves, site-, and year-specific optimum side-dress N rates can be determined. The scenario analysis results indicated that this RFR model-based in-season N recommendation strategy could recommend side-dress N rates similar to those based on measured agronomic optimum N rate (AONR) or economic optimum N rate (EONR), with root mean square error (RMSE) of 17 kg ha−1 and relative error (RE) of 14–15 %. It is concluded that combining soil, weather and management information with crop sensor data using RFR can significantly improve both in-season corn NNI and grain yield prediction and N management, compared with the approach based only on crop sensor data. More studies are needed to further improve and evaluate this approach under diverse on-farm conditions.
Forfattere
Annika M. Felton Hilde Karine Wam Adam Felton Stephen J. Simpson Caroline Stolter Per-Ola Hedwall Jonas Malmsten Torsten Eriksson Mulualem Tigabo David RaubenheimerSammendrag
At northern latitudes, large spatial and temporal variation in the nutritional composition of available foods poses challenges to wild herbivores trying to satisfy their nutrient requirements. Studies conducted in mostly captive settings have shown that animals from a variety of taxonomic groups deal with this challenge by adjusting the amounts and proportions of available food combinations to achieve a target nutrient balance. In this study, we used proportions-based nutritional geometry to analyze the nutritional composition of rumen samples collected in winter from 481 moose (Alces alces) in southern Sweden and examine whether free-ranging moose show comparable patterns of nutrient balancing. Our main hypothesis was that wild moose actively regulate their rumen nutrient composition to offset ecologically imposed variation in the nutritional composition of available foods. To test this, we assessed the macronutritional composition (protein, carbohydrates, and lipids) of rumen contents and commonly eaten foods, including supplementary feed, across populations with contrasting winter diets, spanning an area of approximately 10,000 km2. Our results suggest that moose balanced the macronutrient composition of their rumen, with the rumen contents having consistently similar proportional relationship between protein and nonstructural carbohydrates, despite differences in available (and eaten) foods. Furthermore, we found that rumen macronutrient balance was tightly related to ingested levels of dietary fiber (cellulose and hemicellulose), such that the greater the fiber content, the less protein was present in the rumen compared with nonstructural carbohydrates. Our results also suggest that moose benefit from access to a greater variety of trees, shrubs, herbs, and grasses, which provides them with a larger nutritional space to maneuver within. Our findings provide novel theoretical insights into a model species for ungulate nutritional ecology, while also generating data of direct relevance to wildlife and forest management, such as silvicultural or supplementary feeding practices.
Sammendrag
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Forfattere
Volkmar TimmermannSammendrag
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Forfattere
Divina Gracia P. RodriguezSammendrag
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Sammendrag
A key issue in food governance and public administration is achieving coordinated implementation of policies. This study addressed this issue by systematically comparing the governance of animal welfare in Norway and Sweden, using published papers, reports, and legal and other public information, combined with survey and interview data generated in a larger research project (ANIWEL). Governing animal welfare includes a number of issues that are relevant across different sectors and policy areas, such as ethical aspects, choice of legal tools, compliance mechanisms and achieving uniform control. Based on the challenges identified in coordinating animal welfare in Norway and Sweden, relevant organisational preconditions for achieving uniform and consistent compliance were assessed. The results showed that Sweden’s organisation may need more horizontal coordination, since its animal welfare management is divided between multiple organisational units (Swedish Board of Agriculture, National Food Agency and 21 regional County Administration Boards). Coordination in Norway is managed solely by the governmental agency Norwegian Food Safety Authority (NFSA), which has the full responsibility for inspection and control of food safety, animal health, plant health, as well as animal welfare. Thus, Norway has better preconditions than Sweden for achieving uniformity in animal welfare administration. However, in Norway, the safeguards for the rule of law might be an issue, due to NFSA acting as de facto “inspector”, “prosecutor” and “judge”.
Sammendrag
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