Nature Ecrogrow

We address this research gap in our study. Accordingly, to achieve organic farming targets, it is important to understand the reasons for the regional differences and the factors that lead to higher shares of organic agricultural land. The aim of this study is to identify the conditions or combinations of factors that have led to the regional differences in the share of organic cultivated land in Finland. Finland serves as an interesting case study because of its clear regional differences, both in terms of organic farming and other characteristics. We test the empirical validity of the categorisation developed by Ilbery et al. , with the addition of one economic factor. Ilbery et al. suggested that three groups of factors – physical, structural, and socio-cultural – affect the regional concentration of organic farming. Therefore, we consider a variety of potential factors that affect the regional distribution of organic farming, such as a long organic heritage, agricultural sectors, and market diversity. The importance of subsidies is also included as one of the studied factors, as economic incentives may impact conversion decisions and food systems in general . Qualitative comparative analysis is utilised as the research method because it allows to consider the unique features of each reviewed case and enables the assessment of multiple complex causalities as well as different combinations of factors that affect an outcome . Furthermore, Cairns et al. and Verweij and Trell have shown the potential of QCA for spatial research, which supports its use as a method to examine regional differences in organic farming. The organic farming conversion aid scheme began in Finland in 1990, and after 1995 when Finland joined the EU, the share of organic farming began to grow significantly . In 1990, only about 0.5% of Finnish farms were organic; however, within 10 years that share had grown to around 6%. According to Lampkin et al. , the conversion rates in the 1990s can be partially linked to the subsidy levels in Europe. Countries with high payment levels, such as Austria and Finland, plant benches experienced notable growth in organic farming. Lehtim¨ aki and Virtanen stated that the institutionalisation of organic agriculture in Finland was mainly due to economisation.

The Finnish Government set a goal in 2013 to increase the share of organic farming to 20% of the total area under cultivation by 2020 . However, this target was not achieved despite the share of organic agricultural land increasing rather steadily over the past 12 years . In 2019, organic agricultural land accounted for 13.5% of the total cultivated land in Finland . Globally, Finland was ranked 13th in terms of its share of organic agricultural land in 2018 . The number of organic farms has increased at a slower pace than the share of organic cultivated land, with the number of organic farms even decreasing in some years . The expansion of farms explains this development: the average size of an organic farm was approximately 34 ha in 2005 and approximately 61 ha in 2019. However, average farm sizes vary by region . In the Eurostat regional breakdown, these regions correspond to the NUTS3 regions . Although the target for organic farming in Finland was set as a nationwide goal, there are clear regional differences in organic land area as a proportion of the total agricultural area . In 2019, regional shares of organic land varied between 7.2% and 28.8% . Hence, some regions have already exceeded the government’s target, while others remain far behind. The largest average organic farm size is in North Ostrobothnia in Northern Finland. However, the highest organic shares are in Eastern Finland, where population density is rather low and grain yields are smaller than the average for Finnish farms. The population density also indicates if the region is rural or urban, although almost all Finnish regions are predominantly rural.For this analysis, the prime sector designates the production sector that covers the largest share of the utilised agricultural area. In this study, we also focus on mainland Finland. The Åland Islands, a small group of islands between Finland and Sweden, have unique characteristics that are distinct from the mainland, and thus the region is excluded from the analysis.

Overall, about half of the organic agricultural land in Finland is grasslands, about one-fifth is in crops production and the majority of the organic animal farms are beef or dairy farms . Approximately 3% of beef and milk is produced organically, while the corresponding share of organic eggs is almost 7% . Oats are the most common organically cultivated cereal in Finland, representing about 6.5% of the total oats production . Organic farms also produce, for example, potatoes, carrots, and berries . In Finland, the retail sales of organic products more than doubled between 2011 and 2019, increasing from 163 to 368 million euros and accounting for about 2.6% of the Finnish grocery trade . In addition to the regional differences shown in Table 1, regional variation is also evident in the history of organic farming. Development and educational work related to organic farming started most notably in Finland before 1990, but only in a few regions. Prior to 1990, there were several key milestones that occurred primarily in South Savo, Kainuu, and North Karelia, but also in Uusimaa and South Ostrobothnia . In South Savo in the 1980s, the key factors in the development of organic farming were the establishment of active organic advisors, the Mikkeli eco-county, and the Partala Centre for Rural Development for research on organic farming . In Kainuu, an organic farming advisor and the eco-municipality experiment in Suomussalmi created a network of organic farmers who developed organic agriculture in the region . The first university-level organic farming programme began at the University of Joensuu in North Karelia in the mid-1980s . In addition, organic farming was promoted by industry-related associations and education programmes in Uusimaa during the 1970s and 1980s . Before 1990, South Ostrobothnia had established one organic farming association and employed an active advisor . Studies of organic farming should acknowledge the differences between organic and conventional farming, as they may have a clear influence on conversion decisions. In Finland, the notable economic differences between organic and conventional farming include cost structure, crop yields, profitability, and subsidy levels . Statistics demonstrate the differences in grain crop levels between organic and conventional farming: conventional cereal production is more efficient in terms of land use. This is also the case in many other countries .

In Finland, the yield of organic oats, for example, was about 2300 kg per hectare in 2020, and the corresponding number for conventional oats was about 3900 kg per hectare . However, the organic farms in Finland appear to perform better in terms of profitability , an observation that has also been made in other countries . QCA is designed for comparing a small or intermediate number of cases ; it also incorporates both qualitative and quantitative methods. QCA emphasises the unique characteristics and the full complexity of every case . It is also a comparative approach, which aids the identification of similarities and differences between cases. This process can be achieved using a truth table with a data matrix that expresses the possible combinations of causal conditions . Moreover, QCA is an explanatory model that can be used to test a theory with empirical evidence and, significantly, reveal contradictions . In addition, QCA enables the assessment of multiple complex causalities . In contrast to many statistical methods, QCA is not designed to specify a single causal model that best matches the data; instead, it can be used to define the number and character of the different causal models that exist among selected cases . According to Cairns et al. , the QCA method has the potential to examine the complex spatial factors that affect area-level issues. Furthermore, multiple types of data can be used in QCA to enable comprehensive coverage of the studied topic . These significant features support the decision to use QCA in our study. This study employs the fuzzy-set QCA method, and the analysis is performed with fsQCA 3.0 software . More specifically, we use a four-value fuzzy-set technique ; therefore, in line with several previous studies, the data are calibrated into four-value categories . The fuzzy-set technique was selected because of the nature of our data: both the outcome and the conditions are mostly quantifiable. Crisp-set QCA only allows dichotomous values for the factors, whereas fuzzy-set QCA allows scores at intervals between 0.0 and 1.0 . Therefore, the fuzzy-set approach enables us to categorise both the outcome and the conditions more precisely than other QCA techniques, as the conditions are often not clearly present or absent, but something in between. Defining the studied outcome and the selected conditions marks the starting point in QCA. Researchers then produce a raw data table, in which each case indicates a specific combination of conditions and an outcome . It is recommended that a necessity analysis is conducted separately before a sufficiency analysis to test if some conditions are necessary for the outcome to be present . For the sufficiency analysis, the software produces a truth table from the raw data and displays the data as a list of configurations . The Boolean minimization reduces the long Boolean expression to the shortest expression that will uncover the regularities in the data . More specifically, the Boolean minimization eliminates all the irrelevant conditions from the set relation .

The consistency measures of the results indicate the set-theoretical importance of the outcome, and the coverage measures reveal the empirical importance of the results . Overall, the results require some interpretation, potentially in terms of causality. Therefore, rolling bench the interpretation demands a case-oriented review. The studied outcome in this research is the regional organic shares of total agricultural land , which relates to the Finnish national target for organic farming. We selected the causation factors based on the previous literature and the number of variables that would be reasonable in proportion to the studied cases . According to Ilbery et al. , structural , physical , and socio-cultural factors lead to different concentrations of organic farming. In addition, Helenius et al. stated that food systems are affected by several factors, such as socioeconomic and biophysical aspects, and people as actors and decision-makers. Therefore, it is necessary to include several different factors in this analysis. The data on subsidies and market diversity were obtained by conducting a survey. The survey was sent to all Finnish organic producers who had given permission to use their contact information. The survey, which was conducted as an electronic survey in 2015, was complemented by postal surveys and telephone interviews. According to the Finnish Food Authority , there were 4247 organic farms in Finland in 2015. A total of 840 organic farmers answered the survey. By region, 16–23% of organic farms were covered. The organic farmers who answered the survey represented farms of different sizes and types. Approximately 600 farms produced field crops, 205 meat, 95 horticultural products, 41 milk, 24 eggs, and 23 other produce, such as honey. Some of the farms produced several products. Thus, the survey respondents represent comprehensively different type of organic farms in Finland, roughly in proportion to organic agricultural land. The average farm size in our survey was approximately 57 ha; according to the Finnish Food Authority , this was about 5 ha larger than the average for organic Finnish farms in 2015. The respondents had begun organic farming between 1966 and 2014; therefore, answers were obtained from farmers with a wide range of experience. Overall, the survey provided a representative sample of organic Finnish farms. The survey included several questions about the background information of the farm . The research questions, that were mostly structured, concerned the conversion, sales, economics, and estimates of the organic farming development. The most important questions for this study addressed the reasons why the farms were converted to organic and how the sales of their produce were distributed across the different market channels and regions.

Many studies confirmed the changing trend of cropland degradation in the south part of the Mongolian Plateau, and it still needs to be addressed. GGP has planted shrub or woodland in large areas in China and brought the changing land and livelihoods . The GGP was launched in Inner Mongolia in 2000, resulted in a reduction of cropland area , which is in accordance with the findings of our research. Many studies have shown that after the GGP was launched, the ecological environment of the northern farming-pastoral ecotone has been improved to a certain extent, and the vegetation dynamics have shown a significant increase . Recently, related studies have pointed out that the GGP is the most important land use change method to promote China’s vegetation restoration . However, few kinds of research emphasized the influence of GGP on vegetation restoration by remote sensing technology in the farming-pastoral ecotone of northern China.Based on this, our research has qualitatively evaluated the linear relationship between cropland reduction and regional vegetation restoration using NDVI mean value. Several researchers have reported the ability to use NDVI to assess vegetation restoration. Liu et al. introduced the growing season annual accumulative NDVI in the farming–pastoral ecotone over the countries along with the “Belt and Road Initiative” to monitored the vegetation degradation. Qu et al. utilized the growing season NDVI to investigate the spatiotemporal variations of long-term vegetation change and pointed out that land use changes caused by ecological restoration program are the major driving factor for improving vegetation conditions in the Yangtze River basin. In this case, we also monitored vegetation restoration using NDVI mean value generated from all available Landsat archive. GEE’s powerful computing ability contributes to quickly and accurately data processing, which dramatically simplifies data processing steps. Complex human activities will decrease or increase the effect of cropland conversion. In this case, GGP resulted in the agricultural model transformation in ecologically fragile areas,stackable planters such as farming pastoral ecotone in Inner Mongolia, which brought about the increase in NDVI-mean value of 12.19% from 1990 to 2019.

This upward trend is in agreement with the reality of the study area. Therefore, the approach we introduced in this paper can be applied to evaluate the relationship between land use change and the vegetation dynamics in the farming pastoral ecotone of northern China and other areas that require intensive and extensive monitoring of environmental management practices.Livestock production is associated with both negative and positive environmental impacts, ranging from land area and water use to biodiversity and greenhouse gas emissions . However, environmental impact evaluations of livestock typically focus mostly on GHG emissions resulting from production processes and are aggregated over a wide variety of farming systems . Only recently research has included extensive systems in an attempt to capture the overall benefits, such as nutrient cycling or biodiversity conservation, in addition to rural development roles linked to livestock production . In the largest global review of life cycle assessment for agricultural products , the performance of extensive livestock systems is overshadowed as most studies are based on intensive farming systems. Further, as intensive systems generally have lower emissions and land occupation values per product output than pasture-based and extensively managed systems , most environmental assessment research on livestock in Europe is focused on lowering such impacts through production intensification. Whereas such systems may have limited biodiversity or other non-production  benefits , other extensive systems support them. Few studies in the literature include the lowest possible trade-off situation, that is, specific production systems with the lowest overall adverse impact and the greatest  benefits within their bio-geographical context . Further, most studies have focused on mountainous areas, Iberian dehesas or montados, while boreal regions remained unexplored . Therefore, there is a considerable need for a more holistic and nuanced treatment of livestock production systems, which also includes positive environmental impacts of livestock production, particularly in LCA studies .

This study explored a potential lowest trade-off situation through a specific focus on High Nature Value farmlands. HNVs are “areas where agriculture is a major land use and where it supports, or is associated with, either a high species and habitat diversity and the presence of species of European conservation concern” . HNV farmland has been used as an indicator for assessing sustainability in terms of biodiversity and other ecosystem services . The main differences between mainstream production systems and HNV farming systems are the use of permanent semi-natural pastures and low external inputs to a varied degree instead of the use of cultivated grassland for production . Semi-natural habitats, mainly grasslands, result from long-term extensive ruminant grazing or mowing without added fertilizers or other inputs. Due to moderate human disturbance over thousands of years, semi-natural grasslands are characterised by their exceptional small-scale plant diversity , high shares of indigenous and endemic species , and red-listed species . The diversity of frequently endangered invertebrates and fungi is also high . Such pastures and their biodiversity have experienced a drastic decline due to a double threat of abandonment and intensification, leading to their designation as critically endangered habitats as assessed by the EU . In Finland, semi-natural grasslands represent the single most bio-diverse land use on farmland with unique and highly threatened communities . In the boreal region, intensification of grassland production transformed most of the semi-natural areas into cultivated grasslands, with a subsequent decline in biodiversity . Most of the remaining semi-natural pastures survive in coastal areas or on forested land, land that is otherwise unsuitable for arable cropping. A proportion of the semi-natural grasslands are legislatively protected as part of the national Natura 2000 networks. The remaining areas are estimated to remain at levels of only a fraction of a percent of their historic areas at the beginning of the 20th century . Extensive ruminant grazing, as part of production or for agricultural subsidies, is currently the main management tool that preserves semi-natural grasslands . The aim of this study was to assess the environmental sustainability of livestock production on HNV farms in Finland when compared with mainstream production systems. Such sustainability assessments have not been previously performed in the boreal region.

We hypothesised that HNV farms would have similar or higher environmental impacts compared to mainstream production farms, while also maintaining unique biodiversity compared with the mainstream farms. We first assessed the sustainability of 11 HNV farms in relation to environmental criteria such as unique biodiversity, Nitrogen balance, carbon storage, GHG emissions, and land occupation. We then created alternative states for these 11 farms and compared their performance to that of the actual HNV farms.We invited farmers to participate in the study through social media . From these, 15 farmers contacted us. We selected 11 farms that corresponded to beef and sheep HNV farming system type I and excluded equine farms from this study. The HNV farms were situated in nine out of nineteen regions in Finland. The selected farms differed from mainstream livestock production, namely due to the inclusion of semi-natural grasslands in production. Although farmers completed the questionnaire by themselves, we also provided assistance by telephone in most cases. Primary data collected covered the main aspects of grazing livestock, such as breeds, numbers of animals by age groups, grazing intensity, field use, manure management and yield, and other relevant practices on the farms. Based on such primary data, literature, and expert assessment, we modeled the most critical parameters such as live weights, growth rates, or forage intakes that had the greatest potential to influence the model .We used the best available estimates from a diversity of national statistics databases. Averaged yields of the main feed crops, barley and oats, were based on average Finnish production yields of the last 4 years by respective region . We used farmer-reported protein feed purchases. We considered yields of 6.3 t DM/ha for red-clover pastures based on Lehtonen and Niskanen and 1.8 t DM/ha for semi-natural grasslands based on Saastamoinen et al. . Semi-natural grasslands in production were included in the total Utilised Agricultural Area accounted for on each farm as pastures and other field crops. To avoid double counting in the UAA, we included cover crops as a percentage of legumes and adapted the corresponding yield for the field. We assumed 34% of legumes in grass-clover silage fields and 21% of legumes in semi-natural grasslands . To assess the amount of forage intake originated from the semi-natural grasslands, we based our calculations on the following five key parameters: live weight, age,stackable flower pots growth rate and energy requirements for the animals, and metabolisable energy concentration of low-quality forage.

The ME concentrations applied for semi-natural grasslands and pastures were 8 MJ/kg DM and 11.3 MJ/kg DM, respectively . We calculated growth rates based on live weight and age of the animals reported by the farmers for growing bulls, heifers, calves, and lambs considering the particularities of each breed. For any missing values in the questionnaires, we used estimated values from the literature and average values based on information from the questionnaires. No growth was assumed for suckler cows, adult bulls, ewes, and rams. The energy requirement values and dressing proportions applied were based on national estimations for cows, calves, growing bulls, and heifers separately . The livestock breed was accounted for to assess the energy requirements of each animal of the herd.We excluded the extent of semi-natural grasslands from our HNV sampled farms to build the alternative states of each HNV farm. Based on feed intake requirements for livestock under mainstream Finnish production, we calculated the amount of arable land required in the alternative state to maintain the same herd number as the HNV farms. We used the best available national estimates of feed demand and arable land required by the respective livestock type under mainstream production and averaged input data in the form of fertilizers and pesticides . We kept the other farming practices, including grazing period, the same as in the HNV farms. As the alternative-state farms do not have access to semi-natural vegetation, they have to re-organise their arable land and pasture . We assumed that they would re-arrange their own crop field area; meet an increased demand for arable land either by buying or renting crop area for hay, silage, or cultivated pasture; and purchase cereals for feed if necessary. We used average national yields for all the scenarios implied in the study.In our study, HNV farms seemed to reduce nutrient loses, act as carbon sinks, and require less arable land for livestock production purposes, while maintaining unique biodiversity. We illustrated how the exclusion of semi-natural grasslands from production could make a farm more dependent on external inputs and increase its requirement for arable land intended for animal feed purposes. The need for increased external inputs results from modification in livestock diets and sourcing the feed from arable land instead of non-cropped semi-natural pasture, which overall contributes to higher GHG emissions at the farm level. HNV farming systems tended to have low GHG emissions at the farm level compared to alternative farms . Such lower emissions were due to the larger carbon sinks and lower use of mineral fertilizers, amount of feed imports, and proportion of annual crops. However, the variation within GHG emissions at the product level was high among the farms due to differences in farming practices, livestock numbers, and proportion of semi-natural grassland. Farming practices are a key aspect that influences the overall environmental impact of livestock production, as most of the GHG emissions from a product chain occur at the farm gate . For example, the HNV farm with the highest GHG emission value initiated its livestock production recently and retained the animals entirely without selling. Thus, that farm had the lowest yield compared to the remaining farms. This farm receives subsidies for managing semi-natural grasslands in production. Although such a system generates biodiversity  benefits, it comes at a relatively high environmental cost in relation to other environmental parameters, such as GHG emissions. Our results suggested that a reduction in GHG emissions should be addressed at the level of farming practices rather than production systems as a whole.

This decision is based on the goal of minimizing loss of fish welfare and end product quality; aborting an initiated operation is certain to incur an extra load on the fish while the benefit of a quicker response is uncertain. In addition, the vessels may need to recommission before arriving at the emergency location. Recommissioning will depend on organizational resilience and ability to repurpose assets for operations they were not designed for . This may cover change of crew, picking up equipment, supplies, disinfecting the vessel or the likes. Supplementing the emergency response capability with DERVs on stand-by means that there are vessels that are available to respond to emergencies immediately. However, their emergency response contributions still depend on their positions relative to the emergency location and the impact of bad weather conditions. Examples of emergency types for sea-based fish farming and relevant emergency responses are presented in Table 1. The time frame parameter indicates a rough generalization of how long a situation can be sustained before significant fish welfare consequences are experienced, and amount gives an indication of the possible scope of consequences. Fig. 1 shows the development of three example emergencies as the amount of lost fish as a function of time. The shape and steepness of such development functions in relation to the progress of the emergency response determines the amount of lost fish during an emergency. The model evaluates the emergency response of the sea-based fish farming system at regular intervals, Δt RI, over a given period [t0, t0 +T], as presented in Fig. 2.

Emergency response capabilities change as the state of the fish farming system changes with time; therefore, livestock fodder system the first step of the method makes a prediction of how the fish farming system develops during normal operation based on the input for the initial state, task schedules and weather covering the period. Emergency response is thereafter simulated, and three emergency response measures are recorded at the different testing times, also referred to as response initiation times, e.g., t RI 1 in Fig. 2. The first measure is the first response time, defined as the time it takes from response initiation until the first vessel has commissioned and arrived at the emergency fish farm. The second is the response progress, which covers what response activities that are performed and when, for example the times and amounts for when fish is transported away from the emergency fish farm. Finally, the third is the response completion duration, defined as the time from response initiation until the emergency is over, for example when the last fish is pumped up from the emergency fish farm. Both the simulation of the normal operations in the fish farming system and the emergency response simulation in Fig. 2 are discrete event simulations where the system state changes at discrete points in time . A system state can be illustrated as a snapshot of the system, for example, including the position and status of each vessel and the weather conditions at that point in time, so that the development of a system over time can be described by a series of such snapshots. However, because the simulations are event driven, the system state changes do not occur at regular intervals. The system state is constant for the whole period between two system state changes, e.g., between the event at t2 and t3 in Fig. 2. Changes in the system state happens every time a vessel commences or ends a given operation or changes geographical position with more than one nautical mile. Any change in the initial sea-based fish farming system, including changes to the task schedule or the weather time series, will result in a different list of predicted system states. Uncertainty in the evaluation of the emergency preparedness of the system is reduced by applying several sets of historical data for the task schedules and hind cast weather time series.

The emergency response simulation is run once for each simulated emergency event, logging all details of the response. An emergency event is partly defined by the time at which it occurs, thus two identical emergencies occurring at different times are two different emergency events. Hence, every emergency event must be matched with the correct predicted system state for each emergency response simulation. Understanding emergency preparedness is crucial both to ensure good fish welfare and a sound operational practice in sea-based fish farming. The insight gained from model-based simulations enables the stakeholders to quantitatively assess their ability to effectively handle the various situations that might arise, and how to prepare for such situations. Based on the results of the case study, the method can be used to evaluate both the responses to individual emergencies and the general emergency preparedness level of a fish farming system. It can be used to indicate how well a basic operational system is set up for emergency response, and the improvement in emergency response capabilities from having additional emergency response resources. In Table 4, we see that the effect of having a DERV is more significant for the smaller system, which is expected as the relative capacity of an extra vessel is higher than in the larger system, and the emergency does not scale with the system size. Whether the first response times, response progress or response completion durations advocate for additional resources or other measures must however be seen in relation to specific emergency events and their required response times and statuses. A cost-benefit analysis of possible emergency response measures, for instance adding a DERV, would be one way to make such evaluations. However, formulating a cost benefit analysis is not straight forward due to both the cost and benefit side being highly dependent on, e.g., the system boundaries and to what degree a vessel is going to be dedicated. Testing for two different system sizes is of interest because regulations can divide fish farms into geographical areas, e.g., in the case of Norway where there are defined production areas. Biosecurity restrictions related to crossing the production area borders can be both costly and time consuming. This means that response vessels, to a large extent, can be assumed not cross production area borders within the time span of an emergency response situation.

Given quick response initiation the emergency response of most of the tested cases could be characterized as acceptable, based on the time frames of Table 1. For both weather scenarios and system sizes, the longest response completion durations for emergencies up to 3200 tons were in the order of two days. However, for the 12 800 tons emergencies, response completion durations were found to be as high as a week. The case results could be regarded as optimistic bounds as the response strategy made all vessels respond to the emergency event. Also, the results are based on predictions of the vessel activities, i.e., the mission schedules. New missions may suddenly arise, and the weather forecasts are not certain. The further into the future the evaluations go, the more uncertain are the predictions. However, the assumption that commenced operations may not be aborted prematurely might make the vessels less responsive than they are in reality. In a real-life scenario, two conditions are likely to delay the emergency response, making the response times longer than shown in the results. First, the hazard must be identified, and then the appropriate decision makers in the companies must decide to implement response actions. Early detection of HABs is not easy as the identification of the algae type and concentration usually is done by taking water samples and sending them to laboratories for analysis.Systems for early detection based on satellite imaging of algal concentrations, artificial intelligence identification of algae types, and monitoring of the potential for algal blooms are being developed. Potential for algal blooms is evaluated based on secondary indicators such as water temperature, oxygen levels and the level of blue-green algae. After a threat or unwanted event has been identified emergency response resources are not deployed until the appropriate decision makers give the order. In situations like severe HABs, the potential large scale of the required emergency response means that the response is costly and is likely to negatively affect other parts of the business, e.g., occupying company resources that are needed in normal operation. This means that a thorough assessment of the situation must be made before initiating a full emergency response, and action may not be deemed beneficial until the emergency has escalated. Considering the two delaying factors in real-life situations, response time could probably be improved if DERVs were positioned according to real-time assessments of harm potential and the probability of an emergency. Such a problem would resemble the maximal covering problem addressed in Probability of emergency could, e.g., be based on the degree to which environmental conditions favor a HAB, as proposed in .

Analyses of emergency response performance can be useful in understanding and quantifying risk . Enabling operators to show insurers that they reduce the consequences of adverse events can also provide benefits for both parties. Stakeholders should be aware that the method is not meant to give exact information far into the future,fodder system trays rather it is meant to indicate the emergency preparedness level of a sea-based fish farming system. Therefore, a sufficient number of evaluations should be performed, with different input data, so that they trust the results and the value of the information in the results. However, this depends on what the interests of the stakeholders are and what they want to study. If testing for general preparedness, then the uncertainty of task schedules and weather forecasts is less of a problem since hind cast data can be used. If they want to perform what-if analyses on specific emergencies, the evaluation period should not be stretched too far. The current model of agricultural intensification, based on agrochemical inputs, large monocultures and landscape homogenisation, has successfully increased yields, but is associated with severe losses of biodiversity and ecosystem services, even in neighbouring nature reserves. Current trends can only be reversed by a concerted effort to fundamentally redesign farming systems and agricultural landscapes; that is, a paradigm shift in agriculture. Certified organic farming, that is, banning synthetic agrochemicals to achieve sustainability in agricultural systems in general and biodiversity conservation in particular, is often claimed to be the fundamental alternative to conventional farming. However, the contribution of certified organic agriculture to stop the losses in biodiversity appears to be exaggerated in the public perception. In fact, switching from conventional to organic practices increases local species richness by just a third, but leads to considerable yield losses, so that more land is needed to produce the same amount of food. Surprisingly, a wealth of biodiversity friendly measures that can enhance biodiversity and can be implemented in conventional agriculture, have so far been poorly adopted in current agricultural systems. Here, we challenge the widespread appraisal that organic farming is the fundamental alternative to conventional farming for promoting or restoring biodiversity in agricultural landscapes. After considering measures essential for biodiversity-friendly farming, we propose more effective solutions towards biodiversity friendly landscapes and ways to integrate local and landscape scales in existing organic and conventional farming systems as well as in agricultural policies.Certified organic farming can enhance biodiversity when compared to conventional farming. On average, organic farming across the world’s crops increases local species richness by ~34% and abundance by ~50%, with plants and bees benefitting most and other arthropods and birds to a smaller degree. Benefits also vary with crop type and landscape context. Organic farming strives for environmental benefits, sustaining soil fertility and biodiversity, and prohibits synthetic fertilisers, synthetic pesticides, and genetically modified organisms. In particular, the replacement of herbicides by mechanical weeding is important for biodiversity conservation, because higher weed cover benefits many organisms. Practices such as crop diversification, small fields, green manure, low fertiliser input, and restoration of natural landscape elements are often recommended by organic food organisations and can be more prevalent on organic than conventional farms,but they are not formal part of certification regulations.

The livestock manure was piled up without any management at D1 and D2 in Fig. 1a until it was transported for treatment. Three agro-livestock farming areas were additionally chosen in South Korea to validate the suggested hydrochemical index in a similar condition with pervasive agricultural contamination and feedlots and livestock mainly consisting of cows. The bedrock units in the sites A , B and C are Precambrian biotite gneiss, Jurassic biotite granite, and Cretaceous conglomerate and shale, respectively , which were covered with colluvium or alluvium  similar to the study area with weathering products overlying silicate bedrock . The study area and the three agro-livestock farming areas A to C have a temperate climate with four distinct seasons and are influenced by the East Asian monsoon. The average annual rainfall for the past 30 years in the study area and the sites A to C were 1,286 mm, 1,223 mm, 1,371 mm and 1,349 mm, of which 67%, 66%, 64% and 67% occurred from June to September, respectively . In the study year of 2013, the annual rainfall of the study area and the sites A to C were 1,019 mm, 1,092 mm, 1,062 mm and 1,236 mm with 57%, 51%, 62% and 56% of the annual rainfall occurring from June to September, respectively . Food security is a core concern of the United Nations from the Millennium Development Goals to the Sustainable Development Goals . Population growth, supply–demand imbalances, hunger and poverty have always been long term challenges to food security and sustainable development . According to the United Nations , the global population is projected to rise to 9.7 billion in 2050, with a slow rise to 10.9 billion by the end of the century. This would cause food demand to increase by nearly 70% globally in 2050 . Between 720 million and 811 million people worldwide were hungry in 2020, and 9.9% of the population were undernourished .

In addition, as global climate change intensifies and affects plant diversity , drought and extreme weather events have a greater impact on traditional agriculture, hydroponic fodder system threatening agricultural production in large regions of the world . Cultivated land is the material basis for agricultural production which acts as an important carrier of food security and sustainable development by serving various functions, including food supply, environmental regulation, and ecological services . Cultivated land use is closely related to agricultural development, social stability, and ecological security, which directly affects global environmental change and the sustainable development of regional societies and economies . Driven by food demand and agricultural modernization, cultivated land use has been became more intensive around the world. Although it helps to increase food production, but often associated with the use of a large number of agricultural chemicals and waste, and the increase of agricultural water. Intensive use of cultivated land is now generally recognized to be unsustainable and harmful to the environment and caused a potential human health problem . Under the circumstances of increased demand for food and its byproducts, limited potential of new cultivated land, and increased ecological constraints, it is necessary to produce more sustainable products from existing cultivated land and with lower environmental costs, to meet future food demand, protect natural land, and increase services of the ecosystems . Sustainable intensification has been widely discussed as an effective way to coordinate the demand for land products and functions, protect the ecological environment, and reduce marginal land development . Thus, the sustainable intensification of cultivated land use has become an inevitable requirement to resolve the contradiction between increasing food demand and resource and environmental constraints, improve the service value of cultivated land ecosystems, and realize the transformation of regional cultivated land use according to local conditions . Cultivated land use is also the sum of economic, social and ecological relations formed between man and land in the process of agricultural production and development. In the traditional agricultural countries represented by China, whose main agricultural management form is still family management by small farming households.

Farming households are the most basic micro-socioeconomic subjects and independent decision-making units in cultivated land use. They are at the core of the contradictions among population, economy, resources, and the environment . Therefore, as the largest group of cultivated land use in China, farming households’ behavior and activities directly affect the direction of cultivated land use transformation. Rapid progress in urbanization, industrialization and agricultural modernization, rural social structure and economic form is producing transformation and reconstruction. It directly affects the adjustment of agricultural structure, occupational differentiation of farmers, large-scale operation and technological innovation, and finally reflects on the livelihood of farming households, which makes the farming households’ livelihood types gradually show the characteristics of diversity . Consequently, the farming households’ livelihood transition mainly manifested as the fundamental transformation of the occupation or industry that they depend on for survival and life, as well as the evolution of their dependence on agriculture and cultivated land. To be specific, it directly expressed as the gradual transition of farming households’ livelihood types and the increases in the diversification and non-agricultural degree of livelihoods.With more and more support and promotion of SI, a large number of theoretical and empirical studies have been carried out . Targeted SI researches based on land use perspective need to be further enriched and deepened . As the important land for agricultural production and food security, cultivated land should be the focus of SI. As the SICLU becomes the requirement of coordinating the contradiction between growing food demand and resource and environmental constraints . Non-agricultural and diversified livelihoods have become the inevitable trend of farming households’ livelihood transition at present. These are all scientific questions that need further exploration: How to define the concept and connotations of SICLU? How to carry out quantitative analysis and evaluation? How to form a scientific understanding of SICLU from the perspective of farming households? What enlightenment does it bring to the transformation of cultivated land use and protection? Therefore, 359 questionnaires of farming households in the Qufu County of the Shandong Province, China were used as research samples to: 1) clarify the concept and connotations of SICLU; 2) establish a SICLU evaluation system by means of emergy analysis; 3) evaluate the SICLU level of the sample farming households; 4) and explore the differences between farming households with different livelihood types. This study aimed to provide a reference for understanding the process law of SICLU on a microscale and guide various farming households to optimize the mode of cultivated land use. The earliest SI research dates back to a 1983 workshop. It reported on sustainable intensification of tidal swamp lands in Indonesia by the Research Group on Agroecosystems . But its definition and principles were not clear at the time.

The generally accepted definition of SI was formally proposed in the 1990s . It was originally a cooperative project, for the purpose of establishing an adaptive agricultural system, increasing grain production, supporting livelihoods of the rural poor, and paying attention to the sustainability of the environment, society and economy. Over the past decade, SI has become increasingly prevalent . It also has been promoted as a necessary method for food production in the 21st century by the FAO, CGIAR, and other organizations . SI aims to use existing land intensively to produce a greater number of more sustainable products at a lower environmental cost. As a relatively open concept, SI does not require the predetermination of technology, composition, or design. It is also not limited to the specific development path or method . The goals of SI are to improve the ecological environment, increase economic output and social benefit, and create resource-efficient agriculture models with significantly better environmental performance than traditional intensive agriculture . Related studies have evaluated SI from various scales, ranging from global, country, region, farm, to individual households and perspectives by employing substance flow analyses, data envelopment analyses, carbon balances, and emergy analyses . According to the theory of man-earth relationship, cultivated land is the product of long-term human activities and the development and utilization of natural land. Cultivated land ecosystems have developed into cultivated land ecological-economic systems influenced by human activities, which have positive social benefits in ensuring food security, hunger reduction and poverty, evolving into the compound systems of “ecology-economy-society”. SICLU is not a simple addition of sustainable and intensification, but a combination of social, economic and ecological requirements, under the optimal arrangement and combination of cultivated land ecological, economic and social complex systems. It pays attention to the changes of cultivated land use pattern at macro level and the changes of recessive function form at micro level. Therefore, SICLU has been regarded as a complex and sensitive land use optimization activity that has evolved from static conditions to a dynamic balance integrated system . In this process, by positively regulating the land input/output relationship and alleviating the pressure of environmental response, SICLU overcomes the possible negative impact of intensive utilization on the environment considering resource constraints, forcing the land available to provide material production and service in a sustainable way . In addition, SICLU takes into account the instantaneous impact and cumulative pressure of the combined effects of climate change and human activities, to enhance resilience, self-recovery ability, and biodiversity of the cultivated land ecosystems . Intensive management refers to emphasizing the intensity and effectiveness of investments by improving management methods and technologies, scientifically increasing effective investment, changing management methods, fodder system optimizing management modes, reasonably determining management scale and regional layout, and strengthening the entire process management of agricultural production. 2) High yield efficiency refers to emphasizing the yield efficiency and quality by coordinating the spatiotemporal allocation of inputs and outputs, optimizing crop varieties and planting structures, strengthening supporting infrastructure construction, improving agricultural production links, introducing advanced production technology, improving land and labor productivity, and maximizing the comprehensive production efficiency in existing management areas.

Resource saving refers to emphasizing the rationality and scientificity of resource utilization by reducing invalid investments and unnecessary external inputs, coordinating the proportion of resource utilization, scientifically allocating the investment structure, reasonably and efficiently utilizing various resources, improving the efficiency of resource utilization, and avoiding additional waste. 4) Non-degradation of the ecological environment refers to emphasizing the ecological priority and protection by adhering to the principle of ecological priority and considering source control, minimizing negative impacts on the environment, and further improving the environmental carrying capacity and self-healing capacity of the soil ecosystem, enhancing ecological service functions, and protecting biodiversity. 5) Social sustainability refers to emphasizing social fairness and sustainability by coordinating local relations, ensuring food security, protecting farming households’ rights and interests, reducing resource waste, strengthening market mechanisms, ensuring intergenerational fairness and distributive justice, popularizing agricultural technology training, enhancing microcredit, and improving welfare. There are checks and balances and coordination among the five connotations of SICLU . While focusing on and realizing the one connotation, it will also have the impacts on the realization of other connotations. Of course, the impacts can be both positive and negative. To be specific, high yield efficiency is the direct driving force of cultivated land use to the agricultural businesses and intensive management is the desirable main method to improve output at present. This process inevitably takes the cost of resource consumption and interferes with the ecological environment. It is not conducive to the resource saving and the non-degradation of ecological environment. But extensive management or leaving land uncultivated could lead to yield decline. Meanwhile, food security, resource conservation and ecological environmental protection together constitute the foundation of social sustainable development. Therefore, high yield efficiency, resource saving and non-degradation of ecological environment all contribute to the social sustainability. Guaranteeing ecological benefit is the premise of SICLU. It requires strengthening the ability of cultivated land to resist external disturbances and recover itself, restoring and enhancing the ecological service function, and revealing the versatility of cultivated land use in food production, environment renovation, biodiversity protection, landscape shape and so on. 2) Optimizing economic benefit is the necessity of SICLU. It requires building a balanced system of “combination of use and conserve”, strengthening the overall allocation and systematic development of water, soil, seed, fertilizer, medicine, electricity and other factors.

The workshops ends with a plenary session, in which participants are asked whether there is a shared vision about the future farming system. If such a shared vision is present, the discussion on the strategies to select is tailored towards this vision. If not, all possible alternatives and strategies are kept in mind. These strategies for future systems are compared with the strategies that have been implemented in the past and current system, as derived from FoPIA-SURE-Farm 1, to understand what should change. The case study is the extensive sheep farming system located in the Huesca province, Northeast Spain. Huesca covers about 15,000 km2 and two main regions can be distinguished: 1) The Pyrenees and prePyrenees in the North, covering about 6,000 km2 , where agricultural activities are confined to extensive livestock; and 2) the southern part of the province, characterised by the plains of the Ebro depression , where extensive farming , intensive farming and crop farming are present. In Huesca, the number of decreased from 2,902 to 1,018 and the number of sheep from 923,399 to 521,501 . The size of farms has shown an upward trend in the last years. The current size of a herd is between 200 and 1,000 sheep . These trends are a result of the convergence of a range of economic, institutional, social and environmental challenges the farming system is facing. The extensive sheep farming system is highly dependent on EU and national subsidies, and hence, vulnerable to changing agricultural policy goals and increasing bureaucracy and control requirements. Regarding the social challenges, hydroponic channel the case study area suffered a vast population decline over the last century that comes along with a lack of skilled labour, social services and infrastructures.

The low attractiveness of the farming system and the agricultural specialization result in the lack of new entrants. Finally, the extensive sheep farming system is increasingly limited in the access to pastures. The strategies that farmers have been implementing over time to deal with these challenges follow four management patterns, i.e. intensification, extensification, diversification and conservation . In addition to the provision of private goods, such as to ensure sufficient farm incomes and deliver high-quality food at affordable prices the extensive farming system also provides public goods. Grazing helps to maintain and preserve the natural resources contributing to keep soil quality and biodiversity by maintaining landscape heterogeneity . Extensive livestock activity is also important to prevent forest fires by keeping the area clean from dry biomass , which act as fuel in Mediterranean areas . Grazing activities also provide recreational areas demanded by society and keep the rural areas attractive. As a result of the challenges mentioned in the previous paragraph, levels of functions in the farming system are generally perceived to be low . The clear presence of interacting economic, social and environmental domains makes the extensive sheep farming system in Huesca, an interesting case study for studying sustainability and resilience. In addition, there are signs of low sustainability, low resilience and consequently a pending decline of the farming system . The FoPIASURE-Farm 2 workshop was conducted on 14 February 2020 from 9.00 am till 3.00 pm with one break in the middle and lunch at the end. Eighteen people participated in the workshop, of which seven were farmers . The rest of participants belonged to the agri-food value chain , cooperatives and distributors, and public sector , and local public administration.When discussing critical thresholds , participants argued that these were already reached and that the farming system was on the edge of collapse/decline .

When participants resisted to participate individually, the flexibility of the methodology allowed for slightly adapting the procedure in Step 21 . In order to stimulate the discussion and obtain values for thresholds, the trend and current value of the indicators according to the official statistics were presented to participants. In case of disagreement, participants were asked to define the current value of the indicators in a plenary session, which helped the researchers to determine how the discussed values were more or less close to the threshold. Based on the plenary discussions on thresholds, researchers deduced a number of enabling conditions that are needed to maintain the current system in the future. In the next sections, actual levels, developments and threshold levels of function indicators, indicators of resilience attributes and challenges are presented. articipants indicated that the gross margin is the decisive variable that determines whether the farming system is on the edge of collapse or not. Participants indicated that the gross margin threshold of the farms is 25–30 €/head. According to the literature, gross margins in the farming system vary among farms depending on feeding costs, size of herds and aids . This implies that not every farm is similarly close to the gross margin threshold. While the gross margin of the farms in the flat areas is at threshold and beyond , the distance of gross margins to the thershold appears larger in the farms located in the mid-mountain areas. The latter have lower feeding costs than the former because the herd feeding relies almost enterely on the availability of pastures. Herd size in mountain areas used to be higher allowing farmers to benefit from economies of scales.Participants agreed that the current number of sheep has reached the tipping point in the area.

There are currently about 521 thousand sheep heads in the province of Huesca, with a reduction of 43.7% since 2005 . The decrease in the number of sheep in the farming system has not been as sharp as that of the number of farms. The reason that the decrease of sheep number has not been so marked in the last 10 years is because herds of quitting farms have been acquired by the farms that stayed. The strategy of buying sheep from quitting farmers allowed other farmers to increase their margins and remain in the farming system. Pardos et al. found an average increment of 85 sheep per farm from the period 1996–2001 to period 2002–2005. Currently, farmers are investing a great effort and time managing between 500 and 1,000 sheep/shepherd, but the gross margins are not enough to hire new shepherds and increase the herd. Consequently, from now on the number of sheep is expected to decrease with each farm disappearing from the system. All participants agreed that the costs of feeding are strongly related to the availability of pastures. During the workshop, availability of pastures was assessed by looking at the total available surface of pastures . In the province of Huesca the total amount of pastures has decreased by 65% in the period 2003–2018, with a current total of 160,000 ha in the province of Huesca . Participants concluded that the availability of pastures meets the farming system’s needs, especially now the number of sheep has decreased. However, in some areas such as the flat areas and those surrounding the Natural Parks and other protected areas , the access to pastures is limited or nil. Although grazing contributes to modulate the vegetation dynamics , bureaucracy and regulations limit the access to the pastures in the protected areas. Simultaneously, the increasing intensification of the agriculture in the flat areas is limiting the area of grazing lands. Moreover, the intensification of the farming system has led to the abandonment of lands, mainly in the mid-mountain areas. This abandonment causes a simplification and homogenization of the landscape due to the increase of the tree and shrub stratums, which lead to decrease in biodiversity and increase of fires . Participants found it difficult to provide a minimum value of pasture surface they need for grazing, but they pointed out that the authorities must ease the access to pastures as well as compensate for environmental services delivered by the ovine farming system.

Based on the input from participants, the research team estimated that the system is somewhat close to a critical threshold regarding the availability of pastures. According to participants, the lamb consumption should not decrease more than the current level, indicating that the current level in fact is the critical threshold. Lamb meat consumption has declined strongly in the period 2006–2019 , with a current value of 1.3 kg/inhabitant/year . Participants mentioned that in the short term this challenge has a negative influence on the gross margin and the number of sheep, whereas, in the long term, it can lead to the closure of farms. Participants identified several drivers that explain the lowering demand: consumers preferring other type of meats, mainly pork and chicken; disappearing culinary traditions; upcoming vegetarian and veganism trends; and the increasing campaigns against livestock farming influencing the negative perception of the sheep farming system . Overall, decreasing demand is indeed related to urban trends and social-economic conditions such as consumer preferences and family structures . The quality of products from the case study area may give a competitive advantage .Participants in the workshop are extremely worried about the increasing number of wolves and bears. The wild fauna attacks are recent and there are no clear statistics, but there is great concern about the potential impact. Participants did not provide the value of a critical threshold for wild fauna attacks in the ovine farming system. They indicated that the wild fauna attacks are more frequent in the mid-mountain than in the flat areas,hydroponic dutch buckets where the attacks rarely occur. Participants mentioned that the attacks not only negatively affect the profitability of the farm, but also the farmers’ quality of life as attacks imply more time and investments to take care of the herd. Based on the input from participants, the research team estimated that the system is not close to a critical threshold regarding wild fauna attacks. To compensate for the plenary input in Step 2, the research team decided that each participant should individually assess the impact when critical thresholds are exceeded . In a plenary session all participants discussed the effects of exceeding critical thresholds of challenges and interactions between critical thresholds. Overall, exceeding the critical threshold of one of the challenges was expected to lead to moderate to strong decline in performance of main functions and resilience attributes .

Plenary discussion results are presented in detail in Appendix C. In the evaluation step, interactions of thresholds across domains and scales resulted in a vicious circle which explains the expected decline in system functioning when critical thresholds are approached and exceeded . To adequately describe interacting thresholds in Fig. 2, some additional indicators were added that came forward during the discussions with stakeholders. Fig. 2 can be read as a summary of the information provided in the previous sections on thresholds of main function indicators, challenges and resilience attributes. Gross margin, a main function indicator of the system, plays a pivotal role in the interaction of thresholds and affects the number of farms and consequently the number of sheep in the area. Gross margins are directly affected by three main challenges: reducing subsidies, decreasing consumption and increasing feeding costs. Reducing gross margins and the closure of farms further reduces the available workforce, which reinforces the closure of remaining farms directly and indirectly via increasing feeding costs, which is why a lack of labor is seen as a main challenge. The challenge of increasing feeding costs is indirectly affected by increasing occurrence of droughts and wild fauna attacks, two other identified challenges. These challenges reduce the access and use of pastures, a proxy for the resilience attribute “production being coupled with local and natural capital”. Reduced access and use of pastures is eventually leading to shrub encroachment. Shrub encroachment is further stimulated when the number of sheep becomes insufficient to graze all available pastures. From a social perspective, the closure of farms and the decreasing workforce is expected to lead to a decreasing rural population.Instead of providing defined alternative systems on post-its, participants proposed ideas in a plenary session, thus using the flexibility that the methodology is offering. Two main alternative systems, their goals, functions and resilience attributesand enabling conditions came up in the brainstorming.

We anticipate that these three projects can be sustained in the future without additional funding if the scale of the projects remains at the current level. However, if the scale increases, it will be necessary to consider ways to increase funding. Blue carbon ecosystems have been shown to mitigate climate change. However, we should not limit ourselves to these ecosystems when considering ways to mitigate climate change and provide other co-benefits. The scope of blue carbon offset schemes should be broadened to include other ecosystems that can also play important roles in climate change mitigation. For example, tidal mudflats can be viewed as a type of intertidal blue carbon ecosystem . Although they lack large vegetation, their microphytobenthos can absorb atmospheric CO2 and their soils can store the captured carbon. Moreover, similar coastal ecosystems and carbon storage mechanisms can be found in microbial mat systems and coastal sabk has in arid regions. Among potential blue carbon ecosystems, the macroalgal beds and macroalgae aquaculture areas discussed in this study are gaining recognition . However, to the best of our knowledge, worldwide only these three Japanese sites discussed here have been implemented. Although estimates are few and extremely uncertain, macroalgal beds in SCEs may be the largest contributor to the net CO2 uptake rate. Although this study focused on the specific ecosystem service of climate regulation, SCEs provide various ecosystem services, and thus, represent natural capital. Managing this natural capital is vital to take advantage of co-benefits such as food provision, recreation, environmental purification, health, and employment creation,ebb flow tray in addition to contributing to food security, ecosystem integrity, and biodiversity.

However, highlighting climate change countermeasures, in which society is becoming increasingly interested, can help in initiating or accelerating the conservation and restoration of SCEs. Ensuring that the effectiveness and importance of SCEs are widely perceived and understood by various coastal stakeholders, researchers, engineers, and economists is of primary importance when quantifying SCE functions and monetization and ideally crediting the full range of the provided benefits. While considering the cost-effectiveness of SCEs, it is preferable to first quantify all of their functions and consider the trade-offs among them, and then to monetize them according to the results they produce. This method is preferable to basing the monetization of SCEs’ benefits on willingness-to-pay, as determined by questionnaire surveys. The direct evaluation of multi-functionality supported by numerical evidence is more likely to satisfy coastal stakeholders and to help secure public financing or attract funds from private companies and investors. Although the quantitative social impact of blue carbon offset credits is currently minimal, given the potential of blue carbon for mitigating climate change, expanding the volume and enhancing the social impact of credit trading are important. Some future challenges to be addressed are as follows. First, the motivation of both credit creators and buyers needs to be improved. For example, the Japanese government has set a goal to account for blue carbon in the national inventory by 2024. Making blue carbon offset credit schemes contribute directly to such a national indicator can be expected to improve the motivation of credit creators. In addition, for credit buyers, being able to reflect the blue carbon credits they offset in the nationally determined contributions of the Paris Agreement will enable them to contribute directly to the international community’s goals.

Furthermore, it will motivate them to contribute to their own corporate social responsibility as well as other ESG indicators. Second, it is necessary to enhance offset credit transaction products to generate interest from more participants. One idea to increase the number of participants is to present an array of trading products by assessing the economic value of co-benefits and allowing them to be traded together with carbon. Consequently, credit creators can expect to increase their sales proceeds by increasing both the unit price and the transaction volume. In turn, credit buyers will be motivated by the ability to choose trading products that better fit their goals and branding messages. Third, increasing the number of demonstration projects and accumulating good practices will contribute to raising awareness and interest in blue carbon offsetting. In turn, this can further motivate participants. Finally, we should not forget the role of the credit secretariat, which mediates transactions. Currently, because the amount of blue carbon credits traded worldwide is low, it is difficult to maintain the system in a stable and sustainable manner with the income from intermediary fees. Increasing the trading volume and the unit price by increasing the number of participants is thus important for the smooth operation of the secretariat. In addition, a system such as a validation and verification body, which is independent of the credit secretariat as established by JBE, is important to ensure the credibility of the system through enhanced validation and verification. This credibility is essential for increasing the number of participants in the credit system. The protection of biodiversity and ecosystems is an important and key task in maintaining nature conservation. By stabilizing agricultural conditions, it can contribute to the protection of ecosystems. The occurrence of zoofauna is significantly influenced by the structure of vegetation in connection with various agrotechnical interventions and inputs into the soil .

Sustainable agroeco systems must be biologically and ecologically balanced, technically manageable, economically efficient and socially acceptable. The aim should be to reach a compromise between environmental needs and economic efficiency.One of the main goals of sustainable agriculture is to reduce the risk of diseases and pests in crop systems, thus contributing to the protection of the environment. When applying agrochemicals in different types of farming , we must first understand the ecological processes taking place in these types of agroeco systems. Usually, the management of low input agroeco systems is more environmentally friendly and sustainable compared to classical conventional types.The structure of communities, with emphasis on the abundance and dominance of the Carabidae population within agroecosystems are influenced by many synergistically acting factors such as pedological and hydrological conditions, microclimatic conditions specific to each stand, agrotechnical measures, presence of diseases and pests. Knowledge of trends in the communities of Carabidae agroecosystems is essential for assessing their condition and understanding the processes taking place in nature and in a changing climate, which is manifested by frequent fluctuations in climatic events . Highly specialized agrocenoses are exposed to excessive pressure during the entire growing season, e.g. in the form of an increased number of pests. In addition to anthropogenic factors, Carabidae are one of the main groups that significantly contribute to their regulation. Therefore, their roles and function in environmental services cannot be underestimated . The dominance structure of the Carabidae communities clearly reflflects the conditions of the given habitat and their trophic structure changes depending on the state of the environment . Species of the Carabidae family act as effective bioindicators within agroecosystems, they are extremely adaptable, able to colonize almost all terrestrial habitats and geographical locations, with a stable taxonomy. They are useful organisms in agroecosystems due to their role as predators of cultivated plant pests, thereby reducing pest populations. An important role also belongs to the granivorous species that consume weed seeds, which can only be welcomed in agroecosystems.

From the functioning view of the agroecosystems, dominant species play an important role, the spectrum of prey and the degree of trophic specialization also depend on the individual seasons . In addition to the basic factors influencing agroecosystems, two important aspects are currently crucial. In the first place, there are negative anthropogenic factors acting on a local scale, whilst their effects are unpredictable. In addition, there is the phenomenon of global warming, the causes of which are related to human activities . Whether species can survive in agroecosystems depends on many integrating factors, most of the research focuses on the requirements of adult individuals, and on abiotic and biotic factors influencing their survival, larval research is problematic due to the practicality of the research . Agroecosystems include a myriad of species from the Carabidae family, which increase the biodiversity of agroecosystems with their presence, examples are presence of the abundant species Harpalus rufipes, Poecilus cupreus, Pterostichus melanarius, etc. They are so adapted to the anthropogenic influences that their occurrence in agroecosystems affected by human activity is highly dominant. Species richness and abundance of organisms increase with the intensity of habitat disturbances, but if the intensity exceeds certain limits, biodiversity decreases and leads to the overall imbalance of the community. Such disturbances are usually caused by management, which is a decisive factor influencing the populations present, including Carabidae . The aim of the presented study is to evaluate and compare the impact of ecological and integrated arable farming systems on the species composition, spatial structure and biodiversity of Carabidae populations, within selected cultivated crops. Prediction of the richness of Carabidae populations and homeostasis of agroecosystems was also evaluated. Monitored species indicate topical and trophic environmental conditions and serve as part of complex mechanisms.It was recorded during the period considered 7 801 adult carabids belonging to 26 different species were recorded. The number of species during individual years varied between the types of farming and cultivated crops from 11 to 15. The number of registered species tended to decrease, but increased for some species. The values of the total epigeic activity, their abundance and dominance of ground beetles captured at individual sites during this research are shown in Tables 1, 2, 3. Based on the abundance of the results presented in Tables 1, 2, 3 when comparing the implemented farming methods for the observed period for Carabidae biodiversity,flood and drain tray the results are in favor of the ecological type of farming , compared to the integrated type .

Triticum aestivum dominated in the assessment of the impact of the crop type , Pisum sativum , Medicago sativa . We found that the highest biodiversity of the monitored species was usually in crops with denser growth. In terms of management and based on the number, 2019 can be evaluated as the most suitable year. 3 610 individuals were obtained . In 2020, 3 156 individuals were obtained . The lowest abundance was recorded in the first year of the study, when 1 035 individuals were collected . According to our findings, the integrated management system has a positive effect on the number of dominant groups, especially Coleoptera. Their population varies in abundance and species representation depending on the type of vegetation and soil conditions. The impact of crop harvesting, the application of insecticides and herbicides in integrated farming has had a significant negative effect on biodiversity, but organic fertilizers have contributed to increasing their abundance. It can be stated that the identified epigeic groups represent a diversified component of soil fauna, with different adaptations to the soil environment and different sensitivity to stress. In both farming systems over a three-year period representatives of the Carabidae family had almost mirror occurrence, andspecies always recorded a higher dominance. None of the other species was as prominent as Harpalus rufipes. In relation to climatic factors and the year, its occurrence recorded a high level of significance. This macropterous, highly expansive species confirmed the suitability of the environmental conditions, which are suitable for moist to semi-moist, slightly shaded habitats of fields and meadows. Its presence in agrocenosis in relation to other species con- firmed insignificance. Based on our findings, the average dominance of Harpalus rufipes in organic farming was 70.88% and in integrated 75.70%. The open land species Brachinus crepitans was also dominant. Its dominant occurrence was limited to 2019 within the integrated management system and to 2020 in the ecological system and the integrated management . The impact of the year, temperature, precipitation and type of farming was not significant. Its occurrence is not affected by the presence of another species. It is a species characterized by a strong link to the environment. In 2020, Poecilus cupreus species also showed a dominance in ecological management , which together with Harpalus rufifipes act as evidence of adaptation to anthropogenic influences, as their occurrence is higher in agroecosystems affected by human activity, with potential to reduce the populations Limacidae and Agriolimacidae, both adults and their eggs, but also the elimination of an increasing number of aphids.

Our work takes into account a purposive sample of 23 farmers, and the application of the QM has allowed us to identify prevailing discourses whose interpretation contributes to enriching the debate on PF, providing a new perspective on the subject to policy makers. Additionally, the QM can be used to rethink policies for the dissemination of innovative tools, and, in this regard, provide a better understanding of the transfer of innovation to the agricultural sector to improve the effectiveness of innovation policy. Finally, the discourses of this study can provide new insights to boost responsible policies, even more so since RRI applications to PF are quite limited. The article consists of an introduction, followed by the theoretical background in which the transfer of technology for innovation processes is explored. Then, the key dimensions influencing PFT adoption are discussed, including the sphere of the self. In the third section, the methodology is presented, followed by the results. The discussion and conclusions close the work. In the period of agricultural modernization, innovation has been conceptualised as a linear and unidirectional flow of knowledge of a top down type from researchers to farmers. During the 1960s, the innovation process shifted from a “science push” model to a “market pull” model, underlining the role of demand . These approaches, defined as technology oriented, aim to study the innovation process only through technical and economic factors . Over time, these trajectories, strongly disconnected from the needs of farmers and from the context in which innovation operated, led to explorations of more systemic approaches to innovation, such as the agricultural knowledge and information system and, later, mobile grow rack the agricultural innovation system .

In fact, it was only in the 1990s that innovation was conceptualised as a contextualised “networking process”, implying a learning process between actors. It is precisely this new conceptualisation that marked a change from “topdown” to “bottom-up” approaches, where science and technology are embedded within a social and institutional context . The contextual inclusion of “innovation processes”, well explained by Elzen et al. with the term “anchoring of innovation”, has been highlighted for adoption in the PF field . The literature has shown how anchoring mechanisms are an optimal strategy fostering an environment that is conducive to scaling innovation in this field . On the one hand, this evolution reflects the complexity of anchoring innovation processes in agricultural systems; on the other hand, it reflects that farmers’ thinking has played an increasingly active role in innovation processes over time . Hence, there are numerous contributions that researchers have proposed to try to identify the drivers of and barriers to adoption at the farm level. Even though governments, industry and funding agencies have made efforts to persuade farmers of the benefits of PF, adoption has been low or fragmented. Together with the analysis deepening the complexity of the transfer of innovation, researchers have tried to assess the reasons for this low uptake. First, numerous studies have tried to determine the characteristics of adopters and the contextual factors based on which farmers may more easily accept a new technology in their management . Most studies have pointed out that young farmers appear to be more involved in agricultural innovation . The reasons for this propensity lie in the fact that new generations report a higher level of education and, at the same time, a growing need for information, which is similarly positively correlated with adoption, in addition to greater exposure to and familiarity with virtual technologies .

The need to acquire skills in the use of these tools is also combined with the high investment cost of these tools. In fact, with their ability to absorb costs, large farms have been described as being more willing to adopt innovation. Small enterprises can become PFT adopters through contractors or partnerships . At the same time, the labour intensity indicator gives a clearer idea, in relation to the production activity analysed, of how much agricultural activities are accompanied by new tools or whether manual labour is still present.The role of adopters in the context in which innovation operates has been widely investigated in the literature by identifying numerous dimensions concerning not only the structural dimension and farmer perceptions but also the institutional context . In particular, the institutional context includes social and cultural dynamics and environmental and policy aspects . To understand adopters, researchers have explained how the decision to adopt is only partly linked to the structural and institutional dimensions of farms . Among the factors already mentioned, some studies also include the perceptions of farmers. Perception is the result of a subjective assessment made by the potential user regarding the attributes of innovation and the influences exerted by the structural and institutional dimensions in orienting behaviour in the adoption process . Among the attributes, many authors have focused on the perceived relative advantage and, in particular, farmers’ profitability . Others have highlighted that the perception of the technological and organisational complexity of innovation can significantly influence adoption . Many theories have tried to explain behaviour in the adoption process by emphasising the role of perceptions, the figure of the adopter and background factors .

Since the 1960s, the early theories and models of technology acceptance and adoption have emphasised the role of behaviour and perception as key variables in the adoption process. Fishbein and Ajzen’s theory of planned behaviour  and later extensions postulated that the individual’s behaviour is the result of multiple components, such as attitude, subjective norms, and perceived behavioural control. In social cognitive theory , Bandura reports how behaviour, personal factors , and the external environment of the individual are bidirectionally connected in understanding the adoption process. Davis theorised differently in his technology acceptance model that attitudes are the determinants of behavioural intentions to perform an action or not and are based on perceived ease of use and perceived utility . The TAM itself has been extended by exploring the determinants of perceived utility and perceived ease of use, introducing the relationship between them into the structural dimension . These theories are the starting point and lay the groundwork for investigating the links between i) contextual and structural factors, ii) perceptions, and iii) behaviour that could predispose individuals to adopt new technologies. However, in these models, where perceptions or behaviour is taken into consideration, the agent is always considered rational . This is the vision offered by classical economics, in which the actor manifests autonomous and fixed preferences disconnected from the context . In contrast, in behavioural economics or in the field of sociology, researchers have spoken of “quasi-rational actors” and even “enculturated” decision makers, whose perceptions and behaviours are shaped by the context . The perception-behaviour link has been widely recognised in the psychological research field, which addresses how “perceptions guide action but so too do actions influence what is perceived” . The role of the self in this linkage has been highlighted by Jaswal , affirming that “perception-action coupling is not only manifest in the behavioural arena, but also shows up in the internal processes of the agents, particularly those related to the self”. This is confirmed by Markus and Kitayama , who discuss a mutual and dynamic constitution of context and the self. For example, regarding the concerns of the self, perceptions are subjected to profound social and non-social influences exerting lasting effects on the behaviour and in the moment of decision making due to the context to which individuals have been exposed until that moment .

Reimer et al. is one of the few studies that in the field of adoption that analyses how the characteristics of farmers and farms as well as the and farm context can shape the perception of a new technology and, consequently, the individual’s behavioural intentions towards it. The literature shows the enormous efforts made, especially regarding three aspects: codifying the phases of technology diffusion, theorising adoption models, and identifying the major drivers of and barriers to adoption and all its influencing factors. It is possible to summarise the points previously discussed as follows. The QM that we employed in this work is based on the five-step procedure shown by McKeown and Thomas . The five steps are outlined in Fig. 2. To carry out this analysis, the first two steps are the most important; defining the “concourse” and creating the Q set can affect the whole analysis. The former is the raw material of the Q study that provides the “self-referent notions” arising from shared understanding, whose specific meaning may differ depending on the context . Since the volume of the concourse can be infinite, it has to be dimensionally reduced to obtain the Q set, which is the collection of statements related to the most important aspects of the study theme . The sentences included here should represent a variety of different opinions and feelings rather than being limited to concrete facts . Following the procedure shown by McKeown and Thomas , the concourse was built using scientific publications, newspapers, farmer blogs or interviews, conversations, commentaries, and texts related to the subject. From this review, we defined a final concourse composed of 80 statements . Using an inductive approach , the analysis shows that several dimensions influence farmers’ perceptions of PF and its adoption. These dimensions are not a strict categorisation; rather, they represent a guide to ensure coverage of the most relevant aspects related to farmers’ opinions on PF. Several rounds of discussion were implemented among researchers to delete and rephrase redundant and unclear statements. At the end of the described procedure,ebb and flow table the initial list was refined into a more comprehensive Q sample composed of 33 statements. Q samples must be composed of statements that are “natural” in the language of the participants and “comprehensive” in their representation of the subject to provide individuals with the opportunity to best express their personal opinions . Consequently, the use of academic language should be avoided to facilitate understanding, and a balanced number of positive and negative statements should be included to avoid opposites or similar statements .

Small sheets of paper are used to print the declarations, which are also identified with a code that cannot influence the participant during the process. Before being administered to the sample, the test was tested by a collaborator. In our case, the list of declarations was chosen based on the literature on precision agricultural tool adoption, focusing on drivers and barriers. In the third phase, it is necessary to select participants who are theoretically relevant to the research question and who have a defined perspective to express what matters in relation to the topic . This interview method was first tested among the members of the research group to determine the best way to submit the questionnaire. After a test, it was decided to proceed from the socio-demographic questions and then proceed to the Q sorting phase. The P set is usually smaller than the Q sample, typically from 10 to 40 people . The reason for this can be found in an ancient maxim attributed to Roman Emperor Marcus Aurelius, who stated that “the opinion of 10,000 men has no value if none of them knows anything about the topic”, leading us to the choice of a purposive sample of farmers who have at least “heard of” PF . Therefore, an intentional sample of 23 farmers was selected. The interviews were conducted by two researchers who selected the respondents based on the question “Have you ever heard of innovation, technological innovation, or precision farming in agriculture?” This allowed us to select only those agricultural entrepreneurs who had the necessary conditions to carry out our questionnaire. The interviewees were asked to voluntarily participate in the study. No financial compensation was promised or subsequently awarded. They were informed of the objectives of the investigation, the duration of the interview, and the possibility of abandoning the investigation at any time, and they were given the contact details of the principal investigator for any clarifications or indications on the matter.

The resilience capacities define the possible range of actions to maintain the desired functions of the farming system, i.e. the provision of private and public goods at desirable levels. The selected courses of action in turn also affect the actors, institutions and resources of the farming system and its enabling environment, constituting a feedback loop. Resilience is a latent property of a system. The concept denotes a potential which is activated – and can be observed – only when a system is hit by stress or shocks . It can thus be understood by learning from past trajectories and discussing future scenarios, and from assessing how actual shocks are dealt with . The first approach was used in a systematic assessment of sustainability and resilience over the course of 2017–2020. This provided insight into the multiple factors contributing to resilience. We used the second approachwhen Covid-19 hit European food and agricultural systems. This allowed us to compare the resilience attributes of the system and the resources and institutional support from the enabling environment that were activated to respond to challenges before and during the Covid-19 crisis. The 11 farming systems have been analysed since 2017 in the SURE-Farm project, which has been funded under the EU research program Horizon 2020 and aims to understand and systematically assess the sustainability and resilience of farming systems. Qualitative data on the farming systems during the Covid-19 crisis were collected by members of the SURE-Farm consortium in their respective countries in spring 2020, focussing on exposure to restrictions and sensitivity of the farming system, actions taken by farming system actors in response to restrictions, the role of the enabling environment , stacking pots and discussions and reflections triggered by the crisis .

Due to the short time frame to plan data collection, different methods were used depending on availability and feasibility in each case study. In most case studies, interviews were complemented with a review of media and policy documents . Each case study team interpreted the data with a focus on the anticipating, coping and responsive capacities displayed by the actors in the farming systems, the agility of the actions , the degree of fragmentation or connectedness across actors and the display of leadership, i.e. which actors shaped the interpretation of the situation, and provided guidance and coordination . The findings on the Covid-19 crisis were then compared to previous insights for each farming system, using selected findings from the systematic resilience assessment. These included findings on resilience capacities, the role of the enabling environment, prevailing challenges, and systems’ performance of resilience attributes such as diversity, profitability and openness.Major exposure and sensitivity were observed in the extensive sheep farming system in Northeast Spain and in the small-scale mixed farming system in Northeast Romania, mainly due to severely interrupted sales to restaurants and peasant markets, respectively. In the small-scale mixed system in Northeast Romania also milk collection was interrupted. A medium level of exposure and sensitivity was observed in the fruit & vegetable system in Mazovia. Here the travel limitations for foreign workers created problems. The other farming systems were exposed only to minor degrees. For instance, the dairy system in Flanders faced lower prices, but could continue production and delivery, and in other farming systems the timing of the lockdowns was relatively fortunate, i.e. not affecting harvests but during tillage season or after seeding and planting . In the intensive arable system in Veenkoloni¨en and the extensive cattle grazing system in the Massif Central important markets were barely affected.

Despite only minor exposure and sensitivity in most farming systems, a wide variety of actions was undertaken across all farming systems . Similarities were financial support programs from governments and attempts to set up online-sales channels and home-delivery services. Also, in many farming systems, cooperatives became active. For instance, in the extensive sheep grazing system in Northeast Spain cooperatives kept farm-gate prices at a reasonable level through stimulating national consumption and by developing new markets. In trying to solve shortages of foreign workers, farmers’ associations in the fruit & vegetables system in Mazovia successfully anticipated and started to contact Ukrainian workers directly via Facebook platforms, while the German Farmers’ Association organised flights for migrant workers, among others from Romania and Bulgaria. The Spanish government ensured availability of shearers from Uruguay. In contrast, in the UK the government tried to mobilise local workers, such as through the ‘Pick for Britain’ and ‘Student Land Army’ initiatives, and in the egg & broiler system in South Sweden unavailability of migrant workers was coped with by hiring furloughed labour from companies in the region. Impacts were overall minor . For instance, in the fruit & vegetables system in Mazovia the speed of arranging availability of Ukrainian workers and the switch to less labour-intensive crops reduced the system’s medium exposure and sensitivity to a minor overall impact. The early signalling of the upcoming labour shortage by the farmers’ organisation seemed a pivotal anticipating capacity. Some actions also reduced a system’s exposure and sensitivity. For instance, the agile efforts of Belgian dairy processors to cooperate in order to ensure continuation of milk collection has been an important factor leading to relatively minor consequences in the Flanders dairy system. A somewhat more nuanced view on impacts came from some farming systems which recognised that impacts were unevenly distributed across actors, depending on membership of a cooperative and entrepreneurship . Also, despite minor impacts in the short term, some actors in arable systems expressed concerns about long-term consequences on price levels .

Most of the long list of actions undertaken by farming system actors and the enabling environment suggest coping capacities. This is especially pronounced for the actions undertaken by the enabling environment; only in the hazelnut system in Lazio and the extensive sheep grazing in Northeast Spain the government was partly responsive through changing physical field inspections to georeferencing and by actively engaging in identifying new export markets respectively. We observed more responsive actions at the level of farming systems; in the large-scale arable system in Northeast Bulgaria and in the extensive sheep grazing system in Northeast Spain even the majority of actions by farming system actors were responsive . Anticipation was quite rare and was observed only in the dairy system in Flanders where processors anticipated through crisis protocols, in the arable system in the Altmark where some farmers anticipated and responded by early buying of inputs, and in the fruit & vegetables system in Mazovia in relation to the availability of foreign workers. Although few actions could be classified as responsive behaviour, the discussions and reflections triggered by the crisis dealt with a range of topics which would require fundamental changes in farming systems or food and agricultural sectors in general. Discussions related among others to calls for more self-sufficiency, shorter value chains, reduced dependence on migrant labour, improved fairness and inclusiveness in value chains, more cooperation among farmers, and more innovations . Not much variation in agility could be observed; where needed, actions were taken swiftly . Only in the Hazelnut system in Lazio it was reported that decisions were taken promptly, but that the actual implementation of related actions was slow. Regarding leadership, more differences were observed across farming systems . In the three farming systems with the highest exposure and sensitivity, leadership was taken by actors from the enabling environment in the fruit & vegetables system in Mazovia and in the mixed farming system in Northeast Romania, while in the extensive sheep grazing system in Northeast Spain actors from the farming system itself led important actions. In other farming systems, leadership was jointly taken by actors from the farming system and the enabling environment.

Connectedness was mostly apparent at the level of processing cooperatives or farmers’ associations . Little connectedness was found in the large-scale arable system in Northeast Bulgaria, the extensive beef system in Massif Central, and in the small-scale mixed system in Northeast Romania. In the latter, lack of cooperation along the value chain and between farmers was seen as rooted in the communist history and considered a major problem in developing solutions during the lockdown. In Romania the lack of cooperation was also among the discussion topics . Revealed resilience capacities during the Covid-19 crisis largely coincided with the resilience capacities from the pre-Covid assessment, i.e. also before Covid-19 there was a focus on short-term robustness as indicated by the frequent ‘b’ in Table 4. However, there were a few exceptions. For instance, in the arable system in Northeast Bulgaria and the arable farming system in the East of England the pre-Covid-19 focus of farming systems was on coping capacities while the Covid-19 situation revealed mainly responsive capacities. With regard to actions taken by the enabling environment, the opposite was true in among others the mixed system in Northeast Romania and the egg & broiler system in South Sweden, i.e. there was more focus on supporting coping capacities during Covid-19 than before. A comparison of pre-Covid-19 challenges and those observed during lockdowns shows that a number of challenges persisted during the lockdowns . , observations reported in Table 3 and discussion topics summarised in Appendix B.For instance, each farming system in which labour shortage was already identified as a top- 5 challenge in the pre-Covid-19 assessment also reported labour issues during the lockdowns . Interestingly, in three farming systems respondents reiterated their worries about climate change, i.e. in the arable system in Northeast Bulgaria, the arable system in Veenkoloni¨en, and the fruit & vegetables system in Mazovia, as they feared that exposure, sensitivity and impact of climate change would be much larger than from Covid-19. The Covid-19 crisis also revealed a number of additional challenges . These related to financial distress in the arable system in Northeast Bulgaria and mental stress in the arable system in the Altmark. Actors in three systems also reported problems due to collapse of agritourism activities , while such diversified activities were usually assumed to be less vulnerable to external shocks than agricultural production activities. For the mixed system in Northeast Romania and the sheep grazing system in Northeast Spain also the interrupted sales were an additional challenge. With regard to system characteristics that enhance resilience, grow lights connectedness stood out . Vice versa, lack of connectedness constrained resilience actions. The latter was illustrated by the small-scale mixed system in Northeast Romania in which low connectedness of small farms with value chains hindered small farms to access retail chains when peasant markets closed or were no longer visited by consumers .

System characteristics however did not explain all patterns of Covid-19 resilience actions . In two farming systems we observed that pre-Covid-19 connectedness among farmers was high, but this did not play a role during the Covid-19 crisis. In the hazelnut system in Lazio individual farmers took actions, not the cooperative. Also, in the beef system in Massif Central processors took leadership. The opposite was observed in the fruit & vegetable system in Mazovia where pre-Covid-19 connectedness was low, but the Covid-19 crisis revealed that farmers’ and labour organisations were well able to take joint actions to quickly ensure the availability of Ukrainian workers. In this paper we assessed how and why farming systems in Europe were able to cope with Covid-19. We did so by assessing exposure and sensitivity of farming systems, actions undertaken by farming systemactors and their enabling environment, leadership, connectedness, agility of actions and overall impact. We also assessed discussions triggered by the crisis in media and among stakeholders. Short-term impacts were then compared with pre-Covid-19 knowledge about the farming systems, including systems’ resilience capacities, the role of the enabling environment with regard to resilience, the range of pre-existing challenges and the performance of resilience attributes. In most cases, few anticipatory capacities were observed, even when the impending pandemic became plainly visible through media reports in early 2020. All systems then displayed adequate agility to activate coping capacities. Related actions were led by farming system actors or the enabling environment, or both. Agility was mainly based on already existing connectedness among farmers and more broadly in value chains. Across cases, the experience of the crisis triggered reflexivity about the operation of the farming systems.

According to those findings, there was an elevated nitrogen content in the shoot tissue of the plants, while plant growth was inhibited; in the current study the elevated level likely reflected an increase in stress responses upon exposure to MG. Several plants species have potential for dye decolorization. The decolorization of either the textile effluents or dye mixtures used can be achieved by adsorption and accumulation on plant surfaces, and mostly by phytotransformation or phytodegradationdthe mechanism that degrades or transforms the dye into non-toxic products. The degradation could be enhanced by rhizosphere-associated microorganisms, by enzymes excreted from or within roots  or even by enzyme extracted from leaves  and cell cultures. In the current study, the adsorption of the MG dye to root surfaces, as could be seen by the blue staining, particularly in the treatments with MG of 2 mg/L or 4 mg/L, could be one mechanism that accounts for the depletion of MG dye in the growth solution that occurred in this study. According to Davies et al.,adsorption of xenobiotics followed by its absorption, allows the binding of xenobiotics to plant roots. Retarded roots of B. chinensis growing with MG at 2 mg/ L or 4 mg/L suggests toxicity of MG to plant roots. It has been reported that B. juncea has great potential for Reactive Red 2 degradation which is supported by the activities of the enzyme laccase and NADH-DCIP reductase predominantly present in roots. Nevertheless, Mukherjee and Das  reported that the decolorized level of MG by Enterobacter asburiae Strain XJUHX-4TM decreased as the exposure time and concentration of the dye increased due to the toxicity of the dye to bacterial cells.However,hydroponic nft the 28 d of exposure used in the current study and at higher concentrations  may have caused a reduction in the detoxification ability of the plant and resulted in plant toxicity.

The results obtained from the FTIR analysis can be used as a tool to predict the changes in the functional groups of the original dye molecules. The ATR-FTIR analysis was performed in the study to detect whether or not there were functional groups possibly obtained from MG that had accumulated in the edible plant part. The ATR-FTIR spectra comparison between plant samples from the control and treatment groups suggested that at the concentration of 1 mg/L, MG may be transformed before either being taken up by the plants or translocated into the shoot tissue. Kagalkar et al.  showed that Blumea malcolmii Hook. could degrade MG dye and the degradation gave 4-dimethylaminocyclo-hexa-2,4 dienone as the transformed product. Fu et al.  found that transgenic Arabidopsis converted Crystal Violet to Lleucocrystal Violet,which is non-toxic to the plant, and LCV was then gradually degraded by other endogenous enzyme activities. The products obtained from the phytotransformation of MG were usually non-toxic to tested plant species in all phytotoxicity reports. Hence, this might explain the unaffected growth of B. chinensis in the growth medium with a concentration of 1 mg/L MG, whilst the ATR-FTIR spectra  suggest that there was a similar functional group in MG and in the shoot of plants grown at 2 mg/L MG. Although it might be possible that the functional group originated from MG or could have been obtained from MG degradation, it could also be the substance synthesized by the plant in response to MG. Hence, identification is still needed of the substances, using techniques such as high performance liquid chromatography mass spectrometry. Together with this result, the effects on root growth at MG concentrations of 2 mg/L and above suggested that these concentrations are toxic to root cells and may result in the accumulation of toxic substances in the shoot tissue of B. chinensis. The increased oxalate content in the shoot tissues of plants exposed to MG in the current study might be accounted for by an enhanced tolerance mechanism in the plant, as Nilratnisakorn et al.  suggested that the precipitation of metal-dye complexes in leaves and roots as calcium oxalate, calcium silicate and silica in Typha angustifolia Linn.  is the mechanism that avoids damage to plant cells.

With regard to the potential health risk of some bioaccumulated substances in food products, the accumulation of possibly toxic derivatives obtained from MG transformation such as Leucomalachite Green  in plant tissues still needs to be identified. In addition, as oxalate comprises 75% of kidney stones  and consumption of high oxalate foods can promote the risk of kidney stone formation  in the human urinary tract, B. chinensis grown with MG contaminated water in this study, having increased the oxalate content, may pose a risk of kidney stone formation as well. The results of plant growth revealed that B. chinensis was able to grow in water contaminated with MG at a concentration of 1 mg/L and had the ability to remove the dye from contaminated water through adsorption via its root surface. The tissue contents, total N and total oxalate concentrations, and the ATR-FTIR spectra analyzed in the current study indicated that the tolerance of the plant to low levels of MG could be achieved by increasing stress responses and the accumulation of toxic substances in oxalate form, which hamper toxicity to plant cells. However, the plant could not tolerate high concentrations  of MG resulting in the increased accumulation of toxic substances in plant tissue and the reduction of overall growth as a consequence. A conclusion from the current study is that although the integration of hydroponic plant production for wastewater management in aquaculture that is still using the dye at a low concentration can be applied without noticeable phytotoxicity symptoms, this might pose a potential health risk for humans. Hence, the detection and identification of substances accumulated in plant tissue is still needed.As one interviewee from the Ministry of Agriculture noted “is organic farming sustainable? We think conventional farming is producing sustainable as well and we will support this”. This can be further illustrated by the example of the continued manure problems in the livestock sector, which resulted in a strong exceedance of phosphate emissions in 2018. Based on EU rulings, the Dutch government had to decide reducing the dairy livestock sizes and numerous animals had to be slaughtered. However, organic farmers felt not to be responsible for this problem, while also an expected shortage of organic manure was expected.

In 2018 organic farmers’ associations started a lawsuit against the Dutch government, supported by positive findings of the EU commission on Environment. However, strong resistance came from the conventional Dutch agricultural association which led to the governmental decision not to handle organic livestock differently . As a result, also many organic dairy cows were slaughtered. Regarding the 19 reported barriers, two barriers were identified as high priority , eight barriers a medium priority and seven barriers a low priority . Within the function market formation that accounted for 37% of all barriers, the barrier ‘lack of demand’ was mentioned most often; by 10 of the 13 respondents, and with 77% of high priority. Within the function guidance of the search, one barrier had a high priority , and four had a medium priority. The barrier of the ‘vision on economic growth and export’ was mentioned by 9 of the 13 respondents. Within the function resource mobilization three barriers were identified; two with medium priority and one with low priority. Function F1 accounted for only one medium priority barrier. The functions F2 and F3 had one medium priority barrier and the function F7 one low priority barrier.The observed barriers may lead to systemic problems in the upscaling of organic dairy farming, since they relate both to soft and hard institutional failures . From the first national organic memorandum the Dutch policy vision was to develop the demand side while regular market mechanisms would result in a larger supply and hence an increase of organic dairy farmers. Newspaper articles published at that time were very critical regarding the implementation of the policy. Those articles stated that the Minister of Agriculture relied heavily on market forces and it was questioned whether it could lead to upscaling while price differences between organic and conventional goods remained high. A hard institutional failure can be found in the interplay between the Ministries of Agriculture and Environment . While the Ministry of Environment embraced the sustainability targets of organic farming it did not support this with policy instruments, while within the Ministry of Agriculture the incumbent socio-technological regime blocked specific support to organic farming .

Moreover within the functions ‘market formation’ and ‘entrepreneurial activities’ persistent capacity and capability problems can be identified. First, the lack of consumer demand and lack of stimulation of the consumer were strong barriers. Although earlier organic products could only be purchased through a few specialty shops, this was no longer the case in the second half of the 1990s. Yet from 2010 onwards, newspaper articles also reported a lack of organic supply, and supermarkets had to import organic dairy from other EU countries. Despite this imbalance, some newspaper articles as well as a number of respondents indicated low consumer willingness to purchase organic products due to higher prices. Second, the ‘free-market’ approach also led to a capacity problem of farmers or how Smith states it, a transition problem. This problem was mainly enforced by a hard-institutional failure of lacking transition subsidies, and a soft institutional failure of lacking moral support to farmers during the transition stage. Danish respondents rated the factor ‘goals and initiatives’ an 80% priority as the Danish government has facilitated strongly the development of the sector. By 1986, the Danish Ministry of Agriculture showed an explicit interest in organic farming. This led amongst others to administering of the red Ø-label, hydroponic channel providing subsidies for farmers and a strong support for development and innovation initiatives . Farmers not only received subsidies for the transition phase, but also received environmental subsidies . In 1995 Denmark introduced its first national action plan to promote organic farming. The progress of this action plan was monitored closely and led to a considerable increase in cultivated areas. In 1999 a second action plan was announced with the main goal obtaining a 10% share of cultivated agricultural land . In 2011 ‘The Organic Action Plan 2020’ was introduced. The main goal of this action plan was to double the organically cultivated area by 2020. To realize this plan stakeholder involvement was a necessity. By 2007 this led to a gradual shift from only ‘supply side’ subsidies towards more ‘demand side’ subsidies. More funding was allocated to research, sales promotion, and purchase subsidies for local government canteens, kitchens and hospitals to supply 60% with organic products . Moreover, on the ‘supply side’ also pesticide taxes were introduced that had a direct but moderate effect on the organic sector . Since the implementation, organic farmland has grown by 57% and organic retail sales doubled . Due to these implementations the organic sector went from a small group of self-regulated farmers to a large group of strong legally regulated farmers . Also Austrian respondents rated the factor ‘goals and initiatives’ key in the development of the organic sector . Austria joined the EU in 1995, and faced a low competitive agricultural sector . This was due to the less productive mountainous environment that also resulted in relatively small household farms. The government therefore prepared the sector by “promoting conversion to organic farming as a general strategy for the survival of Austrian agriculture”. Well before the EU accession, farmer organizations, such as the ‘Ernte’ association, developed organic principles independently from the EU regulation . Since its accession and from 2001, Austria continuously implemented organic action programs. These action plans were established to enhance the development of the Austrian organic agriculture sector. Currently the 5th action plan is in place to maintain Austria’s largest share of organic farmland within the EU . In 1987, Denmark introduced conversion subsidies for organic farmers. According to 80% of the respondents, these subsidies enhanced the growth of the organic dairy sector in Denmark. Between 1989 and 1994 the subsidies were mostly aimed at livestock producers .

Recent studies point out that the distribution coefficient of organic pollutants, such as PCBs, on MPs increases with hydrophobicity . In our study, MPs had high levels of dioxins and PCBs while barely detectable levels of pesticides. Dioxins and PCBs have logKow values ranging from 7 to 8 , while pesticides tend to be less hydrophobic. For example, aldrin and dieldrin have logKow values ranging from 5.68 to 7.4, and 4.32 to 6.2, respectively . Thus, hydrophobicity could partly explain the sorption patterns of POPs on MPs observed in this study. PET and PVC MPs placed for 3 months close to salmon farms showed significantly higher levels of POPs than HDPE MPs. Sorption of POPs to MPs depends, in addition to the properties of the POPs mentioned previously, on the properties of the MPs and the water or other matrix surrounding the MPs. Such important properties ar size, crystallinity degree, polarity, colour, occurrence of specific functional groups and surface area of the MPs as well as pH, salinity, temperature of the water and biofilm formation around the MPs . MPs used in this study were non-coloured. PE, PP and PET MPs were similar in size and shape, while uPVC MPs were smaller . Nevertheless, the levels of POPs sorbed to uPVC MPs were similar to the levels found on PET MPs, suggesting that the MP size was not the main factor influencing the amount of POPs sorbed to the different polymers. In addition, PET and PVC in presence of water tend to acquire positive and negative charges, respectively , suggesting that polarity might not play a significant role on the sorption of POPs in this study. The crystallinity of MPs, by contrast, varied among polymers. Crystallinity of polymers is an important factor that affects the sorption of POPs. Crystalline polymers have a well-ordered and firm structure that does not favor sorption of chemicals.

HDPE is characterized by a relatively high crystallinity , while PET and PP are considered semi-crystalline polymers,ebb and flow tray and PVC has amorphous structure. Thus, differences in the sorption of POPs to the four polymers studied may be explained by the degree of crystallinity. However, the level of crystallinity of a polymer can vary considerably as a result of the production process , which could explain differences observed between our study and earlier reports. A study carried out in California, USA, found that HDPE, LDPE and PP MPs deployed for several months in San Diego Bay had significantly higher levels of PCBs and PAHs than PET and PVC MPs . In another study, PE MPs collected in Japanese coastal areas had higher amount of PCBs adsorbed than PP MPs, although the concentrations of PCBs in single pellets from same locations had a high variability . Differences in water temperature, salinity and biofilm formation could also explain the discrepancies with those studies, which were carried out at lower latitudes. Our study was carried out north of the Arctic Polar Circle during the winter, with low seawater temperatures and when the lack of light reduces biofilm growth . To our knowledge there are no previous studies that report sorption patterns of POPs in MPs in waters above the Arctic Polar Circles or in Arctic waters. Thus, comparison with previous studies is not straightforward. Furthermore, studies focused on the mechanisms of sorption of pollutants on MPs have primarily focused on laboratoryscale experiments, which can only heed limited known factors that might influence sorption behaviour. Such cannot sufficiently explain all the mechanisms by which MPs sorb organic pollutants under complex environmental conditions. Several types of pollutants that can exert synergistic or antagonistic effects on one another exist in the natural environment, and the interactions between MPs and pollutants become very complex with the constant changes in environmental conditions.

The above highlights the need to further investigate interactions of chemicals and MPs in diverse regions of the planet to better understand the impact of MPs in the environment. This study focused on the sorption of dioxins and PCBs to MPs and this is, to our knowledge, the first report to show that MPs can bind relatively high levels of dioxins close to salmon farms. The group of POPs evaluated in this work might therefore be a relevant factor for the differences observed between this study and other reports. Previous studies have mainly analysed the sorption of PCBs, brominated flame retardants, pesticides and PAHs on MPs . Very few reports are available on the levels of dioxins bound to MP polymers in the sea. To our knowledge, only one study has reported levels of PCDD/ PCDFs on MPs and such pollutants were only detected in charred MPs collected at the coast of the Maldives . Different types of pollutants can have different affinities to polymers, as shown, inter alia, in our study. For instance, PAHs and chlorinated benzenes were reported to sorb stronger to PE than PP, while PP had higher sorption capacity than PE for hexachlorocyclohexanes . Thus, pollutants with higher affinities to polymers may out compete other pollutants. For example, in a mixture of DDT and phenanthrene, it was observed that the first chemical out competed the latter in terms of MP adsorption . This process could potentially explain the non-correlation observed between the composition of POPs found in the mussels and the MPs. Bio accumulated levels of dioxins in mussels placed next to the MPs for three months in the sea were different to those sorbed to the MPs. Mussels are sentinel species often used to bio-monitor aquatic pollution since they are regarded to generally accumulate pollutants present in the water and have low bio-transformation capacity . Thus, pollutants found in their tissues tend to reflect those found in the surrounding environment.

One possible explanation for the different levels of POPs in MPs and mussels in waters with a cocktail of pollutants could therefore be the competitive binding of pollutants to plastic. In terms of fish farming, this study suggests that PET and PVC MPs could have a higher environmental impact than HDPE MPs. PET and PVC are high-density polymers . Since their densities are higher than seawater , these MP polymers tend to sink and accumulate in benthic sediments , unless they are very small, in the nanoplastic scale, where floatability might vary . Benthic areas beneath fish farms are usually enriched with organic waste, resulting from fish faeces and non-eaten feed pellets, and associated pollutants, which are likely fed on by wild benthic feeding biota. Sediment beneath fish farms could, therefore, be potential sources of polluted MPs to wild organisms. However, the impact of MPs derived pollutants on resident organisms is probably insignificant compared to the exposure to the same pollutants through other pathways , although polluted MPs could represent a environmental threat if carried to non-polluted areas by, for instance, ocean currents. PP MPs also sorbed a significant amount of POPs associated to fish farming. PP has a lower density than seawater and PP MPs will therefore remain for longer periods in the pelagic zone . Thus, PP MPs could have higher capacity to transport POPs from aquaculture facilities to the surrounding areas than other MP polymer types . Furthermore, PP is used in fish farming materials such as mooring ropes, which eventually release MPs as a result of wear and tear . Thus, the impact of this polymer in the environment and in relation to aquaculture could be more important than previously expected. Based on this study, HDPE MPs might play a less important role as vector of POPs from aquaculture facilities to the environment. However, because feeding pipes in salmon farms are a known source of HDPE MPs to the environment , and because the vast majority of materials used in fish farming are made of HDPE, this MP type might still play a significant role in spreading pollutants from fish farms.

Moreover, weathering of plastic and changes in the degree of crystallinity of polymers in the environment could modify the sorption patterns observed in this study . Considering all the above, the role of MPs as potential vectors of pollutants from aquaculture facilities should be studied more in depth and considered in future assessments of the environmental impact of fish farms with open-nets. The role of MPs as vector of such pollutants to organisms or other environments is still a controversial matter . Some studies have shown that pollutants sorbed to MPs can be transferred to organisms under specific conditions. For instance, Murray River rainbow fish exposed to MPs spiked with PBDEs bio-accumulated greater amount of such pollutants compared to individuals exposed to virgin MPs . Other studies have reported that exposure of organisms to pollutants sorbed to MPs is insignificant compared to exposure of pollutants through other pathways, 4×8 flood tray such as diet or environmental exposure . Furthermore, it has been suggested that MPs might reduce bio-availability of such compounds in polluted environments by sorbing pollutants in the water . The aim of this study was to evaluate the potential of MPs to sorb POPs associated with fish farming and, consequently, to act as a possible vector of such pollutants. Our results show that the composition of POPs sorbed to MPs placed for three months next to two fish farms were similar to that of MPs incubated with fish feed for three days, but were significantly different to MPs placed for three months in a harbour and the reference station. This suggests that MPs found in the surroundings of salmon farms can sorb POPs present in the fish feed. However, the ability of MPs to transfer POPs from fish farms to organisms will depend on several factors that were not addressed in this study. Pollution levels in the surrounding environments, ocean current dynamics in the area, species affected or even size and shape of MPs are some important factors to assess when studying the transfer of pollutants from MPs to organisms, which requires further investigation. Therefore, the role of MPs in transfering POPs from salmon farming remains uncertain based solely on our results.

Modern day agriculture in Europe has evolved towards a highly industrial sector by intensification and farm scale enlargements in order to contribute to global food production . The produced commodities compete on world markets resulting in low consumer prices, but also forcing farmers to continuously decrease costs and increase yields through technological innovations and management intensification to maintain their competitiveness . Although food production has considerably increased, it has also led to many adverse impacts on the environment and biodiversity . As a response and triggered by societal pressure, a wide spectrum of sustainable forms of agriculture has been developed over time . These sustainable production systems depend less on external and synthetic inputs and may result in reduced environmental degradation and biodiversity conservation. In many instances, forms of sustainable agriculture start as grassroot movements initiated by social interests . Today, many types exist but are relatively immature to study a long-term sustainability transition . Organic farming emerged in Europe in the early 20th century largely independently by private activities . From 1991 it has been ‘institutionalized’ by the establishment of a European wide organic regulation, the EC Regulation 2092/91 . This replaced most national policies which were established in the 1980s . The regulation of 1991 was repealed, and the current organic legislation falls under council regulation EC NO 834/2007 . For the period from 2014 to 2020 the CAP provided funding for organic farming through the European Agricultural Fund for Rural Development . Each EU country implements their own Rural Development Programme specifically tailored to their own challenges and capabilities . Currently, the European Commission has set out an ambitious action plan for the further development of organic production by member states towards 25% of organic agricultural area by 2030 . Due to the relatively long history, the long term sustainability transition of organic farming can be well studied. Interestingly, despite the more than 30 years of EU legislation and a common internal market, organic farming in EU member states has developed at different rates .