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.
2020
Forfattere
Hainie Zha Yuxin Miao Tiantian Wang Yue Li Jing Zhang Weichao Sun Zhengqi Feng Krzysztof KusnierekSammendrag
Optimizing nitrogen (N) management in rice is crucial for China’s food security and sustainable agricultural development. Nondestructive crop growth monitoring based on remote sensing technologies can accurately assess crop N status, which may be used to guide the in-season site-specific N recommendations. The fixed-wing unmanned aerial vehicle (UAV)-based remote sensing is a low-cost, easy-to-operate technology for collecting spectral reflectance imagery, an important data source for precision N management. The relationships between many vegetation indices (VIs) derived from spectral reflectance data and crop parameters are known to be nonlinear. As a result, nonlinear machine learning methods have the potential to improve the estimation accuracy. The objective of this study was to evaluate five different approaches for estimating rice (Oryza sativa L.) aboveground biomass (AGB), plant N uptake (PNU), and N nutrition index (NNI) at stem elongation (SE) and heading (HD) stages in Northeast China: (1) single VI (SVI); (2) stepwise multiple linear regression (SMLR); (3) random forest (RF); (4) support vector machine (SVM); and (5) artificial neural networks (ANN) regression. The results indicated that machine learning methods improved the NNI estimation compared to VI-SLR and SMLR methods. The RF algorithm performed the best for estimating NNI (R2 = 0.94 (SE) and 0.96 (HD) for calibration and 0.61 (SE) and 0.79 (HD) for validation). The root mean square errors (RMSEs) were 0.09, and the relative errors were <10% in all the models. It is concluded that the RF machine learning regression can significantly improve the estimation of rice N status using UAV remote sensing. The application machine learning methods offers a new opportunity to better use remote sensing data for monitoring crop growth conditions and guiding precision crop management. More studies are needed to further improve these machine learning-based models by combining both remote sensing data and other related soil, weather, and management information for applications in precision N and crop management.
Forfattere
Krzysztof KusnierekSammendrag
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Forfattere
Krzysztof KusnierekSammendrag
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Forfattere
Krzysztof KusnierekSammendrag
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Forfattere
Divina Gracia P. RodriguezSammendrag
The site-specific nutrient management (SSNM) strategy provides guidelines for effective nitrogen, phosphorus and potassium management to help farmers make better decisions on fertilizer input and output levels in rice (Oryza sativa) production. The SSNM fertilizer recommendations are based on the yield goal approach, which has been frequently cited in empirical studies. This study evaluates the assumptions underlying the SSNM strategy for rice in the top rice-producing countries around the world, including India, Indonesia, the Philippines, Thailand, and Vietnam. Using a generalized quadratic production function, I explore whether major nutrients are substitutes as inputs and if there are complementarities between inorganic fertilizer and soil organic matter (SOM). The results suggest the relationships among major nutrients vary across sites—some inputs are complements, some are substitutes, and some are independent. The SOM also significantly affects the nitrogen fertilizer uptake. I conclude by suggesting that the SSNM strategy can be made to be more adaptive to farmer’s fields if these relationships are accounted for in the fertilizer recommendation algorithm.
Sammendrag
This chapter emphasizes the need for active stakeholder engagement right through from strategy development to planning and implementation, to realize the benefits of sustainable bioeconomy development. In general, this varies between regions and countries. In the EU, it is considered important to engage stakeholders at all stages, whereas in developing countries engaging stakeholders so far has not been given much importance when launching new strategies. Stakeholders, including the private sector, research institutions, farmers organizations, the government and non-governmental organizations, all have important roles to play. The chapter focuses on the why, how and what type of stakeholders should be engaged, and the relevant benefits and challenges. It discusses experiences from the EU and other regions where stakeholder engagement (both formal and informal) and participative governance have led to or are necessary for successful and sustainable bioeconomy development.
Forfattere
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The study aimed to extend the static concepts of multiproduct technical efficiency and determinants into a dynamic setting within the input distance function framework. The existing literature in performance analysis of the dairy farms in Norway based on static modelling and thus ignores the inter-temporal nature of production decisions. The empirical application focused on the farm-level analysis of the Norwegian dairy sector for 2000- 2018. The dynamic efficiency allows analysing the performance of dairy farms in regards of inter-temporal optimization of the investment behaviour. The analysis shows that the static model efficiency study in the previous studies underestimate the performance of the dairy farms. The marginal effects experience positively correlated with dairy farm technical efficiency whereas copped subsidy and asset debt ratio negatively correlated to the performance of the dairy farm.
Forfattere
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The objective of this paper is to examine the economic performance of crop-producing farms accounting for unobserved heterogeneity,environmental variables, and regions. The empirical analysis was based on a translog cost function and unbalanced farm-level panel data for 1991–2013 from the 455 crop-producing farms with 3,885 observations (1,004observations from the central region and 2,881 observations from the eastern region). We found that the mean minimum costs were about 93% and 92% of the actual costs for crop farms in the central and eastern regions, respectively.The marginal effects of crop rotation, land tenure, off-farm activity, direct government support, and experience were positively associated with crop farm economic performance. The marginal contribution of these variables on economic performance increased in the years 2000–2013 compared with the years 1991–1999 in both regions.
Vitenskapelig – Performance of the Norwegian dairy farms: A dynamic stochastic approach
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Forfattere
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From a theoretical perspective, it is well stated that the farm's decision on the use of inputs depends on the farmer's ability to make an efficient decision over time. The existing literature in performance analysis of the dairy farms based on static modeling and thus ignores the inter-temporal nature of production decisions. This paper aims to construct a dynamic stochastic production frontier incorporating the sluggish adjustment of inputs, to measure the performance of dairy farms in Norway. The empirical application focused on the farm-level analysis of the Norwegian dairy sector for 2000- 2018. The dynamic frontier estimated using the system Generalized Method of Moments estimator. The analysis shows that the static model in the previous studies underestimates the performance of the dairy farms.
Forfattere
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