The Tea Bag Index—UK: using citizen/community science to investigate organic matter decomposition rates in domestic gardens

Gardening has the potential to influence several ecosystem services, including soil carbon dynamics, and shape progression towards the UN Sustainable Development Goals, (e.g., SDG 13). There are very few citizen/community science projects that have been set up to test an explicit hypothesis. However, citizen/community science allows collection of countrywide observations on ecosystem services in domestic gardens to inform us on the effects of gardening on SDGs. The geographical spread of samples that can be collected by citizen/community science would not be possible with a team of professional science researchers alone. Members of the general public across the UK submitted soil samples and buried standardised litter bags (tea bags) as part of the Tea Bag Index—UK citizen/community science project. Participants returned 511 samples from across the UK from areas in their garden where soil organic amendments were and were not applied. The project examined the effects of application of soil amendments on decomposition rates and stabilisation of litter, and in turn, effects on soil carbon and nitrogen concentrations. This was in response to a call for contributions to a global map of decomposition in the Teatime4Science campaign. Results suggested that application of amendments significantly increased decomposition rate and soil carbon, nitrogen, and carbon: nitrogen ratios within each garden. So much so that amendment application had more influence than geographic location. Furthermore, there were no significant interactions between location and amendment application. We therefore conclude that management in gardens has similar effects on soil carbon and decomposition, regardless of the location of the garden in question. Stabilisation factor was influenced more prominently by location than amendment application. Gardening management decisions can influence a number of SDGs and a citizen/community science project can aid in both the monitoring of SDGs, and involvement of the public in delivery of SDG

Seasonal dynamics of soil microbial growth, respiration, biomass, and carbon use efficiency in temperate soils

Soil microbial growth, respiration, and carbon (C) use efficiency (CUE) are essential parameters to understand, describe and model the soil carbon cycle. While seasonal dynamics of microbial respiration are well studied, little is known about how microbial growth and CUE change over the course of a year, especially outside the plant growing season. In this study, we measured soil microbial respiration, gross growth via 18O incorporation into DNA, and biomass in an agricultural field and a deciduous forest 16 times over the course of two years. We sampled soils to a depth of 5 cm from plots at which harvest residues or leaf litter remained on the plot or was removed. We observed strong seasonal variations of microbial respiration, growth, and biomass. All these microbial parameters were significantly higher at the forest site, which contained 4.3 % organic C compared to the agricultural site with 0.9 % organic C. CUE also varied strongly (0.1 to 0.7) but was overall significantly higher at the agricultural site compared to the forest site. We found that microbial respiration and to a lesser extent microbial growth followed the seasonal dynamics of soil temperature. Microbial growth was further affected by the presence of plants in the agricultural system or foliage in the forest. At low temperatures in winter, both microbial respiration and gross growth showed the lowest rates, whereas CUE (calculated from both respiration and growth) showed amongst the highest values determined during the two years, due to the higher temperature sensitivity of microbial respiration. Microbial biomass C strongly increased in winter. Surprisingly, this winter peak was not connected to high microbial growth or an increase in DNA content. This suggests that microorganisms accumulated C and N, potentially in the form of osmo- or cryoprotectants or increased in cell size but did not divide. This microbial winter bloom and following decline, where C is released from microbial biomass and freely available, might constitute a highly dynamic time in the annual C cycle in temperate soil systems. Highly variable CUE, which was observed in our study, and the fact that CUE is calculated from independently controlled microbial respiration and microbial growth, ask for great caution when CUE is used to describe soil microbial physiology, soil C dynamics or C sequestration. Instead, microbial respiration, microbial growth, and microbial biomass C should be investigated individually in combination to better understand the soil C cycle

Reasons to not correct for leaching in TBI; Reply to Lind et al. (2022)

We believe that correcting for leaching in (terrestrial) litterbags studies such as the Tea Bag Index will result in more uncertainties than it resolves. This is mainly because leaching occurs in pulses upon changes in the environment and because leached material can still be mineralized after leaching. Furthermore, amount of material that potentially leaches from tea is comparable to other litter types. When correcting for leaching, it is key to be specific about the employed method, just like being specific about the study specific definition of decomposition

A Field-Scale Decision Support System for Assessment and Management of Soil Functions

peer-reviewedAgricultural decision support systems (DSSs) are mostly focused on increasing the supply of individual soil functions such as, e.g., primary productivity or nutrient cycling, while neglecting other important soil functions, such as, e.g., water purification and regulation, climate regulation and carbon sequestration, soil biodiversity, and habitat provision. Making right management decisions for long-term sustainability is therefore challenging, and farmers and farm advisors would greatly benefit from an evidence-based DSS targeted for assessing and improving the supply of several soil functions simultaneously. To address this need, we designed the Soil Navigator DSS by applying a qualitative approach to multi-criteria decision modeling using Decision Expert (DEX) integrative methodology. Multi-criteria decision models for the five main soil functions were developed, calibrated, and validated using knowledge of involved domain experts and knowledge extracted from existing datasets by data mining. Subsequently, the five DEX models were integrated into a DSS to assess the soil functions simultaneously and to provide management advices for improving the performance of prioritized soil functions. To enable communication between the users and the DSS, we developed a user-friendly computer-based graphical user interface, which enables users to provide the required data regarding their field to the DSS and to get textual and graphical results about the performance of each of the five soil functions in a qualitative way. The final output from the DSS is a list of soil mitigation measures that the end-users could easily apply in the field in order to achieve the desired soil function performance. The Soil Navigator DSS has a great potential to complement the Farm Sustainability Tools for Nutrients included in the Common Agricultural Policy 2021–2027 proposal adopted by the European Commission. The Soil Navigator has also a potential to be spatially upgraded to assist decisions on which soil functions to prioritize in a specific region or member state. Furthermore, the Soil Navigator DSS could be used as an educational tool for farmers, farm advisors, and students, and its potential should be further exploited for the benefit of farmers and the society as a whole

Assessment of Benefits of Conservation Agriculture on Soil Functions in Arable Production Systems in Europe

Conventional farming (CONV) is the norm in European farming, causing adverse effects on some of the five major soil functions, viz. primary productivity, carbon sequestration and regulation, nutrient cycling and provision, water regulation and purification, and habitat for functional and intrinsic biodiversity. Conservation agriculture (CA) is an alternative to enhance soil functions. However, there is no analysis of CA benefits on the five soil functions as most studies addressed individual soil functions. The objective was to compare effects of CA and CONV practices on the five soil functions in four major environmental zones (Atlantic North, Pannonian, Continental and Mediterranean North) in Europe by applying expert scoring based on synthesis of existing literature. In each environmental zone, a team of experts scored the five soil functions due to CA and CONV treatments and median scores indicated the overall effects on five soil functions. Across the environmental zones, CONV had overall negative effects on soil functions with a median score of 0.50 whereas CA had overall positive effects with median score ranging from 0.80 to 0.83. The study proposes the need for field-based investigations, policies and subsidy support to benefit from CA adoption to enhance the five soil functions.Unión Europea 635201Unión Europea 652615Unión Europea 68927

Development of an Agricultural Primary Productivity Decision Support Model: A Case Study in France

Agricultural soils provide society with several functions, one of which is primary productivity. This function is defined as the capacity of a soil to supply nutrients and water and to produce plant biomass for human use, providing food, feed, fiber, and fuel. For farmers, the productivity function delivers an economic basis and is a prerequisite for agricultural sustainability. Our study was designed to develop an agricultural primary productivity decision support model. To obtain a highly accurate decision support model that helps farmers and advisors to assess and manage the provision of the primary productivity soil function on their agricultural fields, we addressed the following specific objectives: (i) to construct a qualitative decision support model to assess the primary productivity soil function at the agricultural field level; (ii) to carry out verification, calibration, and sensitivity analysis of this model; and (iii) to validate the model based on empirical data. The result is a hierarchical qualitative model consisting of 25 input attributes describing soil properties, environmental conditions, cropping specifications, and management practices on each respective field. An extensive dataset from France containing data from 399 sites was used to calibrate and validate the model. The large amount of data enabled data mining to support model calibration. The accuracy of the decision support model prior to calibration supported by data mining was ~40%. The data mining approach improved the accuracy to 77%. The proposed methodology of combining decision modeling and data mining proved to be an important step forward. This iterative approach yielded an accurate, reliable, and useful decision support model for the assessment of the primary productivity soil function at the field level. This can assist farmers and advisors in selecting the most appropriate crop management practices. Embedding this decision support model in a set of complementary models for four adjacent soil functions, as endeavored in the H2020 LANDMARK project, will help take the integrated sustainability of arable cropping systems to a new level

Harvesting European knowledge on soil functions and land management using multi-criteria decision analysis

Soil and its ecosystem functions play a societal role in securing sustainable food production while safeguarding natural resources. A functional land management framework has been proposed to optimize the agro-environmental outputs from the land and specifically the supply and demand of soil functions such as (a) primary productivity, (b) carbon sequestration, (c) water purification and regulation, (d) biodiversity and (e) nutrient cycling, for which soil knowledge is essential. From the outset, the LANDMARK multi-actor research project integrates harvested knowledge from local, national and European stakeholders to develop such guidelines, creating a sense of ownership, trust and reciprocity of the outcomes. About 470 stakeholders from five European countries participated in 32 structured workshops covering multiple land uses in six climatic zones. The harmonized results include stakeholders’ priorities and concerns, perceptions on soil quality and functions, implementation of tools, management techniques, indicators and monitoring, activities and policies, knowledge gaps and ideas. Multi-criteria decision analysis was used for data analysis. Two qualitative models were developed using Decision EXpert methodology to evaluate “knowledge” and “needs”. Soil quality perceptions differed across workshops, depending on the stakeholder level and regionally established terminologies. Stakeholders had good inherent knowledge about soil functioning, but several gaps were identified. In terms of critical requirements, stakeholders defined high technical, activity and policy needs in (a) financial incentives, (b) credible information on improving more sustainable management practices, (c) locally relevant advice, (d) farmers’ discussion groups, (e) training programmes, (f) funding for applied research and monitoring, and (g) strengthening soil science in education.</p

Reading tea leaves worldwide: decoupled drivers of initial litter decomposition mass‐loss rate and stabilization

The breakdown of plant material fuels soil functioning and biodiversity. Currently, process understanding of global decomposition patterns and the drivers of such patterns are hampered by the lack of coherent large‐scale datasets. We buried 36,000 individual litterbags (tea bags) worldwide and found an overall negative correlation between initial mass‐loss rates and stabilization factors of plant‐derived carbon, using the Tea Bag Index (TBI). The stabilization factor quantifies the degree to which easy‐to‐degrade components accumulate during early‐stage decomposition (e.g. by environmental limitations). However, agriculture and an interaction between moisture and temperature led to a decoupling between initial mass‐loss rates and stabilization, notably in colder locations. Using TBI improved mass‐loss estimates of natural litter compared to models that ignored stabilization. Ignoring the transformation of dead plant material to more recalcitrant substances during early‐stage decomposition, and the environmental control of this transformation, could overestimate carbon losses during early decomposition in carbon cycle models

Moth Outbreaks Reduce Decomposition in Subarctic Forest Soils

Tree mortality from insect infestations can significantly reduce carbon storage in forest soils. In subarctic birch forests (Betula pubescens), ecosystem C cycling is largely affected by recurrent outbreaks of defoliating geometrid moths (Epirrita autumnata, Operophtera brumata). Here, we show that soil C stocks in birch forests across Fennoscandia did not change up to 8 years after moth outbreaks. We found that a decrease in woody fine roots was accompanied by a lower soil CO2 efflux rate and a higher soil N availability following moth outbreaks. We suggest that a high N availability and less ectomycorrhiza likely contributed to lowered heterotrophic respiration and soil enzymatic activity. Based on proxies for decomposition (heterotrophic respiration, phenol oxidase potential activity), we conclude that a decrease in decomposition is a prime cause why soil C stocks of mountain birch forest ecosystems have not changed after moth outbreaks. Compared to disturbed temperate and boreal forests, a CO2-related positive feedback of forest disturbance on climate change might therefore be smaller in subarctic regions

Calculation of the TBI proxies, script and standardized files

&lt;p&gt;The R script to read out the standardized datasheets and calculate the Tea Bag Index from mass losses of green tea and rooibos. Standardized datasheets for woven/nylon (W), non-woven (NW) and biodegradable/plantbased (BIO) bags types are provided.&lt;/p&gt; &lt;p&gt;The R file 'ReadeR' is the file that contains the active commands, that draw from the functions and conditions specified in 'TBIfun' and 'sheetdef'&lt;/p&gt; &lt;p&gt;&nbsp;&lt;/p&gt