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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.

2024

Abstract

Soil pH is one of the soil properties that determines the levels of bioavailability of macro and micronutrients for plant roots. Apple rootstocks are the medium by which these nutrients are absorbed and shuttled to grafted scions. Our experiment was aimed at understanding the phenotypic and underlying genotypic variation of apple roots interacting with the same soil amended to five pH levels (4.5, 5.5, 6.5, 7.5 and 8.5) by monitoring growth and leaf nutrient concentrations (Ca, Cu, Fe, K, Mg, Mn, Na, P, S, Zn, and Ni) of one year old Golden Delicious trees grafted on 28 different apple rootstocks. Data was analyzed as a full factorial of pH and rootstock type. Soil pH amendment was successful as least squares means for the pH main effect displayed expected nutrient content curves for Mo (increasing with higher pH) and for Mn (decreasing with higher pH). ANOVA showed significance for main effects (pH and Rootstock Type) while the interaction (pH × Rootstock Type) was significant only for Mo. Both main effects were significant for Mn, P, and Ca whereas pH was significant for Fe and rootstock type was significant for Cu, Zn, and S. No significant effects were observed for Na, Ni and K. Multivariate analyses using rootstock genotype LS means revealed diverse correlation (pairwise Pearson) patterns when the data were analyzed as a whole or split by pH treatment levels. For example, the genotypic similarity (Pearson pairwise) between K and Mo was not significant at pH level 4.5 (r=-0.342 and p=0.109) whereas at pH 8.5 such relationship was highly correlated (r=-0.547 and p=0.006). Similar results were observed among other nutrients. Dual hierarchical clustering (Ward) displayed different number and composition of clusters according to pH where two main clusters were observed for pH 4.5 and three main clusters for the other pH levels. Rootstocks G.41, G.890, MM.111 and G.935 were tightly clustered at pH 7.5 whereas at pH 5.5 they all fell into different clusters. These results suggest the individuality of the interaction of each rootstock with pH levels with implications on fertilizer management practices and optimum pH and planting amendments specific for rootstock type.

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Abstract

The evolution of soil structure in agricultural soils is driven by natural and anthropogenic factors including inherent soil properties, climate and soil management interventions, all acting at different spatial and temporal scales. Although the causal relationships between soil structure and these individual factors are increasingly understood, their relative importance and complex interactive effects on soil structure have so far not been investigated across a geo-climatic region. Here we present the first attempt to identify the relative importance of factors that drive the evolution of soil structure in agricultural soils as well as their direction of effect with a focus on the temperate-boreal zone. This was done using a random forest (RF) approach including soil, climate, time, and site factors as covariates. Relative entropy, as quantified by the Kullback-Leibler (KL) divergence, was used as a quantitative index of soil structure, which is derived from the particle-size distribution and soil water retention data, and integrates the effects of soil structure on pores from the micrometre-scale to large macropores. Our dataset includes 431 intact topsoil and subsoil samples from 89 agricultural sites across Sweden and Norway, which were sampled between 1953 and 2017. The relative importance of covariates for the evolution of soil structure was identified and their non-linear and non-monotonic effects on the KL divergence were investigated through partial dependence analysis. To reveal any differences between topsoils (0–30 cm; n = 174) and subsoils (30–100 cm; n = 257), the same analysis was repeated separately on these two subsets. The covariates were able to explain on average more than 50% of the variation in KL divergence for all soil samples and when only subsoil samples were included. However, the predictions were poorer for topsoil samples (≈ 35%), underlining the complex dynamics of soil structure in agricultural topsoils. Parent material was the most important predictor for the KL divergence, followed by clay content for all soil samples and sampling year for only subsoil samples. Mean annual air temperature ranked third and annual precipitation ranked fourth for subsoil samples. However, it remains unclear whether the effects of climate factors are direct (e.g., freezing and thawing, wetting and drying, rainfall impact) or indirectly expressed through interactions with soil management. The partial dependence analysis revealed a soil organic carbon threshold of around 3% below which soil structure starts to deteriorate. Besides this, our results suggest that subsoil structure in the agricultural land of Sweden deteriorated steadily during the 1950′s to 1970′s, which we attribute to traffic compaction as a consequence of agricultural intensification. We discuss our findings in the light of data bias, laboratory methods and multicollinearity and conclude that the approach followed here gave valuable insights into the drivers of soil structure evolution in agricultural soils of the temperate-boreal zone. Theses insights will be of use to inform soil management interventions that address soil structure or soil properties and functions related to it.

Abstract

Since the 1950s, the use of plastics in agriculture has helped solving many challenges related to food production, while its persistence and mismanagement has led to the plastic pollution we face today. Soils are no exception and concentrations of polyethylene mulch debris up to 380 kg/ha have been reported in Chinese agricultural soils. A variety of biodegradable plastic products have thus been developed and marketed, with the aim to solve plastic pollution through complete degradation after use. But the environmental conditions for rapid and complete degradation are not always fulfilled, and the risk that biodegradable plastics could also contribute to plastic pollution must be evaluated. In this presentation, we want to share the knowledge gained through research projects on biodegradable plastics in agricultural soil, where we both studied the degradation of biodegradable mulch under Nordic soil conditions, and the fate of other biodegradable plastics in soil amendments such as compost and biogas digestate. A two-year field experiment with biodegradable mulch (PBAT-starch and PBAT-PLA) buried in soil in mesh bags showed that also under colder climatic conditions does degradation occur, involving fragmentation already after 2 months, but that complete degradation may take 3 to 9 years, depending on soil temperature and soil organic matter content (both correlate positively with degradation rate). Accumulation is therefore likely to happen when biodegradable mulch is repeatedly used every year. A full-scale experiment with compostable plastic cups (PLA) at an industrial composting plant, where we followed their fate and conducted metagenomic analysis over 13 weeks, demonstrated the major role played by fungi for a successful degradation of PLA. However, the successful management of biodegradable plastic products largely depends on existing waste management infrastructure. Most biodegradable plastic bags, labelled as compostable and used for food waste collection do not end up in industrial composting plants in Norway, but in biogas production plants. Here, we showed that these plastic bags (Mater-Bi®) are only marginally degraded (maximum 21-33 % mass loss) during biogas production, and likely to end up in biogas digestate and then in agricultural soils, unless digestate is treated to remove plastic residues.

Abstract

Since the 1950s, the use of plastics in agriculture has helped solving many challenges related to food production, while its persistence and mismanagement has led to the plastic pollution we face today. Soils are no exception and concentrations of polyethylene mulch debris up to 380 kg/ha have been reported in Chinese agricultural soils. A variety of biodegradable plastic products have thus been developed and marketed, with the aim to solve plastic pollution through complete degradation after use. But the environmental conditions for rapid and complete degradation are not always fulfilled, and the risk that biodegradable plastics could also contribute to plastic pollution must be evaluated. In this presentation, we want to share the knowledge gained through research projects on biodegradable plastics in agricultural soil, where we both studied the degradation of biodegradable mulch under Nordic soil conditions, and the fate of biodegradable plastics in two major soil amendments: compost and biogas digestate. A two-year field experiment with biodegradable mulch (PBAT-starch and PBAT-PLA) buried in soil in mesh bags showed that also under colder climatic conditions does degradation occur, involving fragmentation already after 2 months, but that complete degradation may take 3 to 9 years, depending on soil temperature and soil organic matter content (both correlate positively with degradation rate). Accumulation is therefore likely to happen when biodegradable mulch is repeatedly used every year. A full-scale experiment with compostable plastic cups (PLA) at an industrial composting plant, where we followed their fate and conducted metagenomic analysis over 13 weeks, demonstrated the major role played by fungi for a successful degradation of PLA. However, the successful management of biodegradable plastic products largely depends on existing waste management infrastructure. Most biodegradable plastic bags, labelled as compostable and used for food waste collection do not end up in industrial composting plants in Norway, but in biogas production plants. Here, we showed that these plastic bags (starch-based polymer) are only marginally degraded (maximum 21-33 % mass loss) during biogas production, and likely to end up in biogas digestate and then in agricultural soils, unless digestate is treated to remove plastic residues.

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Abstract

This study presents the first attempt in Sri Lanka to generate a forest fire risk map covering the entire country using a GIS-based forest fire index (FFI) model. The model utilized seven parameters: land use, temperature, slope, proximity to roads and settlements, elevation, and aspect. All these parameters were derived using GIS techniques with ArcGIS10.4 and QGIS3.16. Data from Remote Sensing sources, particularly the MODIS hotspot real-world dataset, were employed to gather fire count information for the year 2020. Validation was conducted through the merging hotspot technique and kernel density estimation (KDE). The research findings highlight the districts in the Central and Uva provinces, such as NuwaraEliya (10.3 km2), Kandy (2.74 km2), and Badulla (10.41 km2), as having a “very low risk" of forest fire potential. Conversely, districts like Hambanthota (0.1 km2), Kaluthara (0.04 km2), and Kurunegala (0.2 km2) exhibit a “very high risk" of forest fire potential, although it is negligible compared country's total area. Overall, the study suggests that Sri Lanka is not currently facing a significant threat of forest fires and is a “medium risk" of forest fires as 49.49% of land falls under this category. These results are of immense value to relevant authorities, including the Ministry of Wildlife and Forest Resources Conservation, in formulating effective strategies to manage and mitigate forest fire risks in the country.

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Abstract

Soybean pod count is a crucial aspect of soybean plant phenotyping, offering valuable reference information for breeding and planting management. Traditional manual counting methods are not only costly but also prone to errors. Existing detection-based soybean pod counting methods face challenges due to the crowded and uneven distribution of soybean pods on the plants. To tackle this issue, we propose a Soybean Pod Counting Network (SPCN) for accurate soybean pod counting. SPCN is a density map-based architecture based on Hybrid Dilated Convolution (HDC) strategy and attention mechanism for feature extraction, using the Unbalanced Optimal Transport (UOT) loss function for supervising density map generation. Additionally, we introduce a new diverse dataset, BeanCount-1500, comprising of 24,684 images of 316 soybean varieties with various backgrounds and lighting conditions. Extensive experiments on BeanCount-1500 demonstrate the advantages of SPCN in soybean pod counting with an Mean Absolute Error(MAE) and an Mean Squared Error(MSE) of 4.37 and 6.45, respectively, significantly outperforming the current competing method by a substantial margin. Its excellent performance on the Renshou2021 dataset further confirms its outstanding generalization potential. Overall, the proposed method can provide technical support for intelligent breeding and planting management of soybean, promoting the digital and precise management of agriculture in general.