Tag: Agriculture

Researchers from CREA (Italy) have developed an aptamer-based technology to track the fate of microbial inoculants used in agriculture.

Microbial-based products such as fertilisers (now named microbial biostimulants by EU legislation) and biopesticides may support plant nutrition and protection under abiotic and biotic stress conditions and are expected to play a key role in agricultural sustainability in the future. In the last decades, they have received considerable attention from researchers, manufacturers, and farmers, mainly because they might help to reduce the use of chemicals in agriculture, and their application is steadily increasing. Currently, the world market of products containing micro-organisms stands at around $10bn and $3bn for biopesticides and biostimulants, respectively.

However, the inoculation of the soil with such beneficial micro-organisms may affect its native microbial populations, with effects that depend on the soil’s chemical and physical characteristics and the environmental conditions (i.e., climate, agronomic practices, cropping systems, etc.). Furthermore, considering the pivotal role of soil microbial diversity for life-supporting functions, changes occurring to the soil microbial structure after applying microbial-based formulations may affect the overall soil health status with effects which can impact crop productivity, quality, and human health.

Thus, the field application of such products requires their registration at the EU and national levels, together with an indication by the manufacturer of various specifications and analytic methods, making it possible to trace their destiny in the environment and prove their medium- and long-term effectiveness. These aspects are closely connected with the ability of the micro-organism to adapt and persist in the (soil) environment. In this framework, which encompasses scientific, commercial, and regulatory aspects, the development of tools to monitor the introduced microbial species is of paramount importance, particularly to ensure a correct risk assessment in relation to the environment and human health.

The EXCALIBUR project

The EXCALIBUR project aims at deepening our understanding of the mechanisms underlying the soil microbiome changes composition and functioning upon bioinoculant application in horticulture, thus providing a soil biodiversity-driven management strategy for farmers. Innovative fermentation and formulation processes have also been carried out to optimise the efficacy of several novel multifunctional bioinoculants. New potentially commercial products were thus developed, and the results we got so far from the field trials showed that these products could support the common practices that are currently used in horticulture by achieving the same performance level but reducing chemical inputs. Most of the effort is now put into the assessment of soil biodiversity dynamics as well as plant-soil-microbe interactions. Several innovative actions are ongoing, such as the development of predicting models, the biodiversity-based Decision Support System (DSS), or molecular diagnostic kits for a quick but reliable assessment of soil health status. However, one of the project’s main goals was the development of a tool to detect the abundance and monitor the persistence (fate) of the bio-inocula that are applied to the soil, using DNA-based techniques for targeting species-specific gene sequences. Such a tool is considered essential for farmers, confirming successful inoculations and the persistence of bio-inocula in the soil, as well as for regulatory purposes.

Current detection approaches for microbial inoculants

Earlier detection approaches from culture-dependent tools, such as direct microscopic examination, plate profiling, and Fluorescent in Situ Hybridisation (FISH), have provided essential insights into the detection and localisation of target microbial inoculants in soil. These approaches led the way to culture-independent tools addressing the analysis of target microbial species of bioinocula and evaluating the bioinocula effect on microbial communities’ structure and diversity.

The rapid development of DNA and RNA-based analytical methods offered new opportunities to monitor microbial inoculants’ survival and interactions within a specific soil community. Indeed, a high degree of resolution is fundamental to evaluate the success or failure of bacteria or fungi inoculation, tracing the ‘introduced DNA’ in a mixture of genomes from thousands of different native organisms. Culture-independent methods have also effectively characterised the soil microbial assemblages in space and time, evaluating their functional and trophic interactions. Recent research in the ‘omic’ era has expanded our knowledge and understanding of microbial community assembly, but tracing the bioinocula in the soil is still not a straightforward task. Many methods have been developed to enable inventories of microbial species composition and a good understanding of dynamics and processes of biodiversity in bulk or rhizospheric soil, but generally are not suitable to follow the fate of a single species. For example, ‘DNA barcoding’ has been widely used across all life forms, including micro-organisms, to distinguish a species from another. The barcode is derived from a PCR amplicon of a target sequence used to identify (or barcode) a micro-organism distinguishing it from other species. However, DNA barcodes are not error-free, and single-species barcoding needs to be designed based on robust genetic distances to obtain unique and highly discriminant markers. The genetic variability of individual strains, sometimes closely related but different in genomic traits, is exploited to discriminate the individual species but may as well provide inaccurate identifications. Markers based on sequences characterised by amplified regions (SCAR) have been widely used as molecular probes for tracking the fate of fungi. SCAR markers are based on universal primers, i.e., sequences universally present with highly conserved flanking regions, which, however, can discriminate only at genera or species group levels but not at species level within a pool of micro-organisms. However, markers suitable to monitor or discriminate the introduced bioinocula from native soil strains should be species-specific.

In this context, the EFSA (European Food Safety Authority) is working to establish methods helpful in evaluating the risk and traceability of micro-organisms introduced into soil. The most significant difficulty in the research and development of molecular markers to be used in soil relies on the fact of being able to identify markers that are species- or strain-specific, i.e., which can discriminate a species or a strain thereof, from another one in the soil, which represents a heterogeneous and highly complex matrix. Indeed, billions of micro-organisms, many of which are unknown, reside in a gram of soil. Therefore, new strain-specific detection methods are needed.

An aptamer-based detection tool

In EXCALIBUR, researchers Loredana Canfora and Andrea Manfredini from CREA proposed an aptamer-based detection tool as these are successfully applied for clinical purposes and, only more recently, in monitoring food and heavy metal contamination. Still, they have never been used for agroindustry. Aptamers are emerging biosensors based on ssDNA or RNA capture probes that can bind various target ligands with high affinity and are cheaper and more sensitive than antibodies. An aptamer, advantageously enables recognition of the target strain at a cellular level without any need to extract nucleic acids, resulting in a considerable lowering of costs compared to the known methods, both in terms of man-hours and in terms of consumable materials. Furthermore, the use of an aptamer makes it possible to perform an innovative in situ analysis, never carried out in the field of soil microbial inoculants’ traceability.

The idea was thus to select at least one of them by ‘systematic evolution of ligands by exponential enrichment’ (SELEX) method for the diagnostic traceability of microbial-based inocula in soil. The method was developed and validated for the detection of the micro-organism Bacillus subtilis, a bacterial species widespread in soil having the potential function of plant growth stimulation or protection, and exploited in several formulations for agricultural applications. The complete genome of the target strain B. subtilis PCM/B00105 was sequenced to select the species-specific aptamers. Based on a bioinformatic analysis, a specific region of the genome was identified, on which a pair of primers was designed for the selection of discriminating aptamer candidates. The choice and selection of the gene region on which to design the pair of primers were decisive for the efficient and successful definition of unique aptamers (Italian patent n. 102022000022590). To validate the results obtained in vitro, an experiment with soils inoculated with a formulation containing the B. subtilis PCM/B 00105 strain was carried out. It was necessary to optimise the method of extracting the cells from soil samples, as impurities and interferences of soil compounds and soil texture can affect the extraction efficiency. The technique for detecting the micro-organism B. subtilis by means of employing an aptamer-based approach is advantageously transferrable onto a mobile device, for example, using a biosensor.

Lab-on-a-chip

As high-affinity ligands, aptamers can be chemically modified to increase their degree of affinity. This latter characteristic makes aptamers like antibodies, but they are more stable compared to the latter, do not induce immune responses, are capable of being immobilised on inert supports and are not thermolabile. Thus, aptamers can be easily transferred onto a nanoscale ‘lab-on-a-chip’ microfluidic system as opposed to other biomarkers/biosensors that are limited to being applied on a laboratory scale and entail higher costs and very long analytical times. In the EXCALIBUR project, we are trying to develop a biosensor chip-based (lab-on-a-chip, LoC) consisting of the aptamer immobilised and exploiting surface acoustic wave (SAW) technology.

Conclusions

Despite the challenges posed by the soil complex matrix, the successful implementation of modern methods for traceability and monitoring of microbial inoculants in soil is a crucial step towards a better understanding of ecological systems and the correct adoption of practices involving the use of microbial-based products. Knowledge of soil ecology will enable the widening of the opportunities derived from the use of microbial products and ultimately help us protect our environment. By adopting this advanced approach, we can promote environmentally friendly practices essential for preserving our planet’s delicate balance. Moreover, it may be successfully used to monitor any target organism, such as B. subtilis in soil, being practical to optimise bioinoculant application methods, support regulatory processes and foster the shift of agricultural production toward more sustainable cropping systems. In conclusion, using new methods for traceability and monitoring micro-organisms in soil is a vital investment in our future and will benefit future generations.

References

Malusà E, Berg G, Biere A, Bohr A, Canfora L, Jungblut AD, Kepka W, Kienzle J, Kusstatscher P, Masquelier S, Pugliese M, Razinger J, Tommasini MG, Vassilev N, Meyling NV, Xu X, Mocali S (2021). A holistic approach for enhancing the efficacy of soil microbial inoculants in agriculture: from lab to field scale. Glob J Agric Innov Res Dev, 8:176–190. https://doi.org/10.15377/2409-9813.2021.08.14

Beegum S, Das S (2022) Nanosensors in agriculture. Editor(s): Sougata Ghosh, Sirikanjana Thongmee, Ajay Kumar, In Woodhead Publishing Series in Food Science, Technology and Nutrition, Agricultural Nanobiotechnology, Woodhead Publishing, Sawston, UK Pages 465–478, ISBN 9780323919081. https://doi.org/10.1016/B978-0-323-91908-1.00012-2

Manfredini A, Malusà E, Costa C, Pallottino F, Mocali S, Pinzari F, Canfora L (2021) Current methods, common practices, and perspectives in tracking and monitoring bioinoculants in soil. Front Microbiol 12:698491. https://doi.org/10. 3389/fmicb.2021.698491

Manfredini, A., Malusà, E. & Canfora, L. Aptamer-based technology for detecting Bacillus subtilis in soil. Appl Microbiol Biotechnol 107, 6963–6972 (2023). https://doi.org/10.1007/s00253-023-12765-0

Song MY, Nguyen D, Hong SW, Kim BC (2017) Broadly reactive aptamers targeting bacteria belonging to different genera using a sequential toggle cell-SELEX. Sci Rep 7:43641. https://doi.org/10.1038/srep4 3641

Co-authors

Loredana Canfora

Andrea Manfredini

Eligio Malusà

Stefano Mocali

Please note, this article will also appear in the seventeenth edition of our quarterly publication.

Agricultural policies are complex things that must take into account the various changing situations in different areas, and the BESTMAP project will provide a framework to do just that.

The BESTMAP project focuses on developing a flexible, interoperable, and customisable framework that will consider farmers’ needs and effectively model agricultural policy impacts on natural, social and cultural assets in rural areas.

Existing impact assessment models do not appropriately address the complex farmers’ decision-making processes and ignore the wider impacts of agricultural policy on natural, social, and cultural assets in rural areas. Through a bottom-up approach, BESTMAP’s new modelling framework has the potential to transform the design and monitoring of future EU rural policies, promoting a sustainable future for the EU agricultural sector.

Here, we briefly present the conceptual framework of BESTMAP and discuss the lessons learned from this Horizon 2020 project towards an operational pan-European policy impact modelling tool.

Spatially explicit biophysical and socio-economic models were developed in five case studies — the Mulde river basin in Germany, Catalonia in Spain, the Bačka region in Serbia, the Humber in the UK, and South Moravia in Czechia. The aim of modelling was to enable the estimation and mapping of the effects of selected agri-environmental measures on biodiversity (farmland birds), water quality, soil carbon, food production and net farm-added value.

The Farming System Archetypes (FSA) used for modelling represent a generalised typology of farming systems assumed to have similar responses to agricultural policy changes. The FSA framework defines main farm characteristics based on two dimensions: Farm specialisation and economic size. Both dimensions were calculated and mapped for every farm within the case studies using data from the IACS/LPIS data collected by each Member State. Farms belonging to the same FSA are assumed to have similar decision patterns regarding the adoption of Agri-Environmental Schemes (AES).

Additionally, agent-based models were parameterised to explain the adoption of AES by individual farms. The outputs of these models were used to parameterise farm-level regression models, and a novel approach was developed to assess how well these models can be transferred across FADN regions. All outputs were translated into policy-relevant indicators and integrated into policy notes and an online dashboard for data visualisation and decision-making.

For more information about the outputs of BESTMAP project visit www.bestmap.eu

Scaling up the BESTMAP approach

In the BESTMAP upscaling phase, meta-models were created for ecosystem services (ESS) in NUTS3 regions within each case study area. These meta-models used the case study ecosystem service model results as response variables, incorporating expert-opinion-based initial sets of potential explanatory factors from environmental and economic predictors. Employing variable selection techniques refined these variables for each ecosystem service in each NUTS3 region.

The resulting meta-models predicted outcomes for NUTS3 regions across all case study areas, with the coefficient of determination (R2) gauging prediction accuracy. R2 values were then plotted against the differences between case study NUTS3 regions regarding key environmental and socioeconomic variables (the ‘Minkowski distance’) per Ecosystem Service (ESS). This approach helped assess the ‘transferability’ of ESS results to non-case study NUTS3 regions in Europe. Results meeting specific criteria indicated a high level of confidence, allowing the transfer of findings to new regions based on their Minkowski distance.

Some NUTS3 regions in Europe were challenging to predict using the existing methodology due to substantial Minkowski distances from the current case studies. This suggests that for future iterations of BESTMAP, new case studies in different locations would be essential. To optimise resource allocation, additional case studies should ideally encompass and closely represent regions not adequately covered in the initial BESTMAP project. This section aims to identify potential locations and determine the required number of representative future case studies if BESTMAP were to be replicated.

The methodology was split into two parts. The first involved establishing the representativeness of all NUTS3 regions in relation to each other, while the second involved identifying regions where confidence levels were inadequate for the transfer of ecosystem service outcomes based on the existing BESTMAP case studies.

Representativeness of all NUTS3 regions in relation to each other

Hierarchical clustering was utilised to identify NUTS3 regions that were well-represented by others. This technique groups similar regions based on multiple variables. Each data point begins as an individual cluster and is subsequently merged into larger clusters, ultimately forming a dendrogram – a tree-like structure. Dendrograms indicate which regions exhibit the highest degree of similarity and provide an overall picture of how all the regions are connected.

Identifying regions for future case studies

The location of the most useful future case studies required the identification of areas that are not currently sufficiently covered, as determined by the transferability criteria. To identify such regions, any NUTS3 region that met the transferability criteria more than five times for any one of the ESS was excluded.

The resulting map was reduced to 166 cluster combinations. It highlighted different regions where transferability confidence is low, based on the current BESTMAP case studies. These identified regions serve as potential locations for future case studies in order to be able to cover all of Europe in terms of transferring ESS models run at the case study level.

To create a more defined shortlist consisting of five or fewer suitable locations, as addressed by the BESTMAP project, a more refined criterion was employed: Any NUTS3 region that met the transferability criteria more than three times for any one of the ESS was excluded from the map, which gave fewer but more distinct regions. By conducting this further analysis, we identified regions that align with the current project’s goals and have the potential for successful implementation (see Fig. 1). This process suggested future case studies might be in northern Spain, north-west Italy, central Italy, Montenegro/Albania, and Bulgaria.

It is important to note that the outlined methodology represents the initial phase in identifying new case study regions. Equally important is the involvement of local stakeholders, including government officials and communities, to acquire insights into the unique challenges and opportunities specific to each region. These challenges may encompass issues such as obtaining relevant regional-level data, such as LPIS (Land Parcel Identification System) data. The ultimate objective is to select regions that not only align with project criteria but also have the potential for long-term impact and sustainability.

Development and validation of a Europe-wide nutrient run-off model

In BESTMAP, unique ESS models were developed for each case study region. To scale these up and enhance agricultural policy relevance, approaches to assess the transferability of these local ESS models to all other parts of Europe were developed.

BESTMAP has also assessed an alternative approach, applying a single model across Europe, using Europe-wide data sources to parameterise the model. Specifically, we carry out a Europe-wide application of the InVEST Nutrient Delivery Ratio model (NDR) for estimating run-off of nitrogen (N) and phosphorus (P) from agricultural land. This NDR model was also applied in the case studies.

Model output was validated against within-river nutrient concentrations measured under the European Environment Information and Observation Network (Eionet), a partnership network of the European Environment Agency.

The model had a relatively high accuracy in predicting both N and P concentrations in rivers, but with discrepancies at lower predicted values of N and at higher predicted values of P. Further analysis of these discrepancies will allow us to suggest improvements to the models and their parameterisation. The models showed that nutrient losses are large from grasslands with a high stocking rate, such as in the Netherlands, Ireland, western Denmark and parts of France and Germany. Low amounts of nutrient loss are found in regions without widespread intensive agriculture such as in parts of Scandinavia and southern Europe.

Data needs for biophysical modelling in agricultural landscapes

While the situation has slightly improved over recent years with the advent of novel datasets, such as those derived from remote sensing (such as COPERNICUS), the challenges persist, especially when it comes to the availability of data for biophysical modelling in agricultural areas. The complexities of agricultural systems demand detailed and up-to-date information on soil characteristics, weather patterns, land use changes, and crop dynamics.

In many regions, the insufficient sharing of relevant data, the absence of standardised formats, and the reluctance of stakeholders to contribute information continue to hinder the development and refinement of effective biophysical models. It is crucial to prioritise overcoming these data limitations, as enhancing data availability not only benefits the scientific community but also empowers farmers. Improved access to data enables farmers to make well-informed decisions, especially when considering significant changes in their practices.

During the BESTMAP project, significant data sharing challanges were overcome. In particular, with data coming from the Farm Accountancy Data Network (FADN). Here we highlight a few recommendations:

1. Improving the usefulness of the Farm Sustainability Data Network (FSDN) by including more detailed information on land management, precise land-use aspects such as intensity, farming practices, pesticide application, fertiliser application, agricultural yields per crop and area, and land tenure (owned/leased land);

2. Improving access to spatial FSDN data to allow access to micro-level anonymised data;

3. Improving compatibility of FADN/FSDN to other European data, in particular LPIS/IACS system; and

4. Addressing issues related to the sampling/regions used in FSDN – i.e. including better representation of organic farms and involving smaller farms.

Piloting synthetic FADN generation

The Farm Accountancy Data Network (FADN) is a comprehensive database that captures pivotal characteristics within the agricultural landscape and is thus highly suitable for exploring the above questions and hypotheses.

However, including personal and confidential information poses a challenge for widespread utilisation due to privacy concerns and legal considerations surrounding protecting sensitive data. In navigating this delicate balance between widespread utilisation and confidentiality, the integration of synthetic data emerges as a promising technological solution, safeguarding sensitive data, improving the accuracy of Machine Learning models, and mitigating bias.

We harvested the Synthetic Data Vault library (SDV), a comprehensive set of tools that covers the entire analytical pathway, including data preparation, modelling, sampling, quality evaluations and visualisation to generate synthetic data that mimics FADN data.

The results are encouraging: The analysis based on synthetic data retained a similar number of features, conserved the overall ranking of hypotheses-focused models, produced coefficients that are strongly correlated to the coefficients of real data (see Fig. 2), conserved similar frequencies of features we consider essential, and conserved high correlation between coefficients even within a single feature. This suggests we have found a good balance between information content and confidentiality.

Improving agri-environmental policy in Europe

Based on the project results BESTMAP proposes several strategies for improving the effectiveness of Agri-Environmental Schemes (AES) in the EU.

• Improving spatial targeting: BESTMAP proposes the use of archetype analysis to help understand spatial patterns in AES adoption and tailoring policies to specific farming contexts. This approach can enhance AES effectiveness by customising schemes to different farming systems.
• Local co-development of AES: Involving farmers in designing AES can increase uptake and align agricultural actions with environmental goals. Co-design processes allow for testing and optimising measures in the field, maximising ecological and economic benefits.
• Better advisory support: Farmers have indicated a lack of relevant advice when it comes to AES application and implementation. Providing accessible and free advisory services can reduce bureaucratic burdens, equip farmers with necessary skills, and guide them in choosing suitable AES. Advisory services should cover ecological advice and administrative support.
• Revisiting AES payments: Current payment levels based on income foregone may not align with farmers’ decision-making. Higher payment rates reflecting the value of ecosystem services could increase AES uptake. A reevaluation of the payment approach is suggested to allow for payments that support public goods through public funding that goes beyond damage compensation.
• Improving Monitoring of AES Success: Consistent monitoring guidance, stakeholder interactions, and adaptive planning are essential for assessing AES impacts effectively. More comprehensive monitoring data is required to inform future policy decisions.
• New Vision for AES Design: BESTMAP proposes a more adaptive and multi-scale approach to EU agricultural policy, integrating bottom-up AES developed with farmers’ input, enhanced advisory services, targeted top-down AES, and improved monitoring within a flexible policy cycle.

Please note, this article will also appear in the seventeenth edition of our quarterly publication.