A 3D image-based modelling approach for understanding spatiotemporal processes in phosphorus fertiliser dissolution, soil buffering and uptake by plant roots.

Phosphorus (P) is a key yield-limiting nutrient for crops, but the main source of P fertiliser is finite. Therefore, efficient fertilisation is crucial. Optimal P application requires understanding of the dynamic processes affecting P availability to plants, including fertiliser dissolution rate and soil buffer power. However, standard soil testing methods sample at fixed time points, preventing a mechanistic understanding of P uptake variability. We used image-based modelling to investigate the effects of fertiliser dissolution rate and soil buffer power on P uptake by wheat roots imaged using X-ray CT. We modelled uptake based on 1-day, 1-week, and 14-week dissolution of a fixed quantity of total P for two common soil buffer powers. We found rapid fertiliser dissolution increased short-term root uptake, but total uptake from 1-week matched 1-day dissolution. We quantified the large effects root system architecture had on P uptake, finding that there were trade-offs between total P uptake and uptake per unit root length, representing a carbon investment/phosphorus uptake balance. These results provide a starting point for predictive modelling of uptake from different P fertilisers in different soils. With the addition of further X-ray CT image datasets and a wider range of conditions, our simulation approach could be developed further for rapid trialling of fertiliser-soil combinations to inform field-scale trials or management.

Image-based quantification of soil microbial dead zones induced by nitrogen fertilization.

Microbial communities in agricultural soils underpin many ecosystem services including the maintenance of soil structure, food production, water purification and carbon storage. However, the impact of fertilization on the health of microbial communities is not well understood. This study investigates the spatial and temporal dynamics of nitrogen (N) transport away from a fertilizer granule with pore scale resolution. Specifically, we examined how soil structure and moisture content influence fertilizer derived N movement through the soil pore network and the subsequent impact of on soil microbial communities. We develop a mathematical model to describe N transport and reactions in soil at the pore-scale. Using X-ray Computed Tomography scans, we reconstructed a microscale description of a soil-pore geometry as a computational mesh. Solving two-phase water/air model produced pore-scale water distributions at 15, 30 and 70% water-filled pore volume. The N-speciation model considered ammonium (NH4+), nitrate (NO3-) and dissolved organic N (DON), and included N immobilization, ammonification and nitrification processes, as well as diffusion in soil solution. We simulated the dissolution of a fertilizer pellet and a pore scale N cycle at three different water saturations. To aid interpretation of the model results, microbial activity at a range of N concentrations was measured. The model showed that the diffusion and concentration of N in water films is critically dependent upon soil moisture and N species. We predict that the maximum NH4+ and NO3- concentrations in soil solution around the pellet under dry conditions are in the order of 1 × 103 and 1 × 104 mol m-3 respectively, and under wet conditions 2 × 102 and 1 × 103 mol m-3, respectively. Supporting experimental evidence suggests that these concentrations would be sufficient to reduce microbial activity in the short-term in the zone immediately around the fertilizer pellet (ranging from 0.9 to 3.8 mm), causing a major loss of soil biological functioning. This model demonstrates the importance of pore-scale processes in regulating N movement and their interactions with the soil microbiome.

Heavy metal bioaccumulation in honey bee matrix, an indicator to assess the contamination level in terrestrial environments.

The most significant risk factor for organisms living in an environment contaminated by heavy metals is the metal bioavailability. Therefore, an efficient ecotoxicological approach to metal contamination is the measure of bioaccumulation level in target organisms. In this work, we characterized the heavy metal bioaccumulation in honey bees, Apis mellifera ligustica, collected at 35 sites from Umbria (Central Italy). The comparison of our data with selected Italian investigations revealed metal bioaccumulation in honey bee matrix of the same order of magnitude, with Cd showing a higher variability. To generalize the results, we developed a Honeybee Contamination Index (HCI) based on metal bioaccumulation in honey bees. An application of the HCI to the present dataset revealed cases of low (sixteen sites), intermediate (eighteen sites), and high (one site) metal contaminations. The comparison of HCI values from the Umbrian dataset with values calculated for other Italian and European metadata showed that most of the Umbrian sites fell in the portion of low and intermediate contamination conditions. HCI represented a reliable tool that provided a piece of concise information on metal contamination in terrestrial environments. Parallel to this effort, we have determined, the metal concentrations in the airborne particulate matter (PM10) at three regional background-monitoring stations in Umbria. These stations are representative of the average air quality of the areas of the investigated apiaries. A comparative analysis of metal enrichment factors in PM10, and honey bees suggested that the contamination in the bees was related to the PM10 values only to a minor extent. On the other side, a clear enrichment of metals such as Cd, Mn, Zn, and Cu in the honey bees appeared to depend on very local conditions and was probably related to the use of pesticides and fertilizers, and the resuspension of the locally contaminated soils and agriculture residues.