Month: August 2022

Matching data on habitat richness, species information was available for the whole region with the same spatial resolution . While the grid is the same used by the Spanish Inventory of Terrestrial Species , the BDD is a distinct data source with more detailed and up-to-date information for the study area. The most recent Spanish land cover/land use map was then used to determine the percentage cover of agricultural land per 10 × 10 km grid cell, information used in the modelling exercise to control for the share of farmland within the landscape . SIOSE allows an accurate estimation of land cover due to a small minimum mapping unit and because it includes an estimation of the share of a given cover within each polygon, even if it is not the dominant cover within the polygon. In this research, we analysed the relationship between farming systems and biodiversity at the landscape level, using farm-level data on farmers’ practices and available biodiversity data. Overall, our analysis resulted in the identification of seven different FS distributed across Galicia: three cattle-based farming systems, one dominated by annual crops, one where permanent crops prevailed, and two non-specialized FS . Based on farm-level data from the IACS/ LPIS dataset, which includes data on farmers’ management reported under CAP payments, we characterized each individual FS in what concerns their spatially-explicit distribution and respective farming practices, in line with previous research performed in other socialecological contexts . Cattle-based FS were found to prevail in Galicia , followed by Non-specialized FS . While the prevalence of livestock –based farming in Galicia was previously reported , our study discriminated three Cattle-based FS based on a decreasing gradient of intensity from the most intensive in the west, intermediate in the central area, to less intensive farms located in eastern Galicia . Such gradient of decreasing intensity, depicted by decreasing levels of livestock density and shares of forage crops, mobile grow system and increasing shares of pastures, seem to relate to an increasing number of biophysical constraints to agricultural practices, namely increasing altitude, slopes and remoteness .

The biophysical characteristics, along with farmers’ decisions mainly driven by agricultural policies, have been acknowledged as major drivers of FS occurrence . Without a relevant expression in the region, FS dominated by annual and permanent crops were found to have restricted distributions. FS 4, dominated by annual crops, was found in the deep soils and mild slopes of the region of A Limia , whereas FS 5, dominated by permanent crops , was found on productive lowlands located close to the sea or in the valleys of rivers, under Protected Designation of Origin . Finally, two non-specialized FS were found widespread across the whole study area and depicting low-intensity small farms owning a low number of livestock and producing mainly forage . The occurrence of small family farm holdings within complex mosaics and parcels was previously reported for Galicia and related to the failure of land consolidation programmes in a region characterized by the occurrence of biophysical constraints limiting agricultural land use . Farming systems approaches based on spatially-explicit farm-level data allow identifying areas sharing a set of similar agricultural management practices e.g. Ribeiro et al. ; Santos et al. . An analysis of FS at the landscape scale allowed pinpointing seven types of landscapes mostly coincident with the seven referred FS, and thus, as expected, exhibiting similar characteristics and distribution patterns across the region, with the exception of LT 2, characterized by a mosaic where FS 2 and FS 3 co-occur. This fact stems from changing the scale of analysis from farm to landscape level, which allowed capturing transition landscape types, specifically reflecting the co-occurrence of intermediate to extensive cattle-based FS along the western-eastern gradient of decreasing intensity. Nevertheless, the coherence between results obtained at the regional and landscape scales highlight the suitability of IACS and LPIS datasets to support the assessment and monitoring of the impacts of farming practices on the environment at multiple scales of decision .

The relationship between the occurrence of species and habitats, including those of conservation concern, and specific farming systems, has been previously advocated . An increasing number of habitats was found associated to a gradient of decreasing intensity from LT1 to LT3 . Higher values of total habitats richness were also recorded within LTs dominated by non-specialized and permanent crops . Still, no significant differences between LTs were found when modelling habitat richness across LTs , except for landscapes dominated by non-specialized farms and dominated by annual crop farms , for which lower values of total and priority habitats richness were observed, respectively. In both cases, lower richness of habitats was coincident with landscapes where the area occupied by farming is higher. Our results are partially supported by Rotch´es-Ribalta et al. that recently reported increasing richness of semi-natural habitats with decreasing levels of farming intensity when analyzing two agricultural landscapes in Ireland. In a European study, García-Feced et al. also reported higher abundance of semi-natural habitats in Northern Spain in marginal farmlands in mountain areas under less intensive farming practices. Landscapes dominated by low-intensity farming systems have been shown to support higher levels of protected species diversity when compared to farmlands under intensive management . Overall, increasing richness of protected species was observed for birds, reptiles, and mammals along the western-eastern gradient of decreasing agricultural intensity, in line with the results achieved for habitat diversity . Our results are in line with previous research. Maskell et al. , observed higher diversity of birds of conservation concern in areas exhibiting higher habitat diversity. Gentili et al. found a decrease of small mammals’ diversity with increasing agricultural intensification and resulting loss of landscape naturalness and complexity in farmlands in northeastern Italy. Biaggini et al. reported low diversity of reptile assemblages in landscapes dominated by intensive agriculture located in Central Italy. Contrastingly, trends for increasing amphibian richness were observed across a gradient of increasing intensity, from landscapes dominated by extensive cattle farms in the east to intensive cattle farms in the west . Still, such patterns relate to global patterns of their distribution in the territory , where higher amphibian richness is observed in western coastal areas and nearby mountains and are related to higher levels of rainfall and lower thermal amplitudes. Relevant differences were detected when modelling richness across cattle dominated LTs, showing higher bird, mammal, flora and habitat richness in landscapes dominated by extensive cattle farms . Bird richness was also found to be significantly higher in landscapes where annual and permanent crops and non-specialized farms prevail when comparing with cattle-based FS . Significant differences were also observed within cattle-based LTs, with the higher richness associated to extensive farming.

The observed pattern may be related to lower intensity of agricultural management of non –specialized farms , reflected by higher landscape heterogeneity due to natural and/or semi-natural areas and uncultivated patches occurrence embedded in the agricultural matrix e.g. see . The significant association between higher bird richness and LTs dominated by annual crops relates to their specific location: A Limia . A Limia is characterized by the occurrence of continental wetlands and coincides with an area designated for conservation of bird diversity – the SPA ES0000436 ‘A Limia’. Thus, the observed pattern is likely due to the coexistence of farmland and the wetland system, rather than to the occurrence of the farmland itself. In fact, farmlands in the area resulted from the process of land reclamation of wetlands that occurred in the late 1950s . Still, significant remnants of the wetland system exist, allowing the occurrence of very vulnerable steppe birds . Finally, modelling results depicted decreasing richness of mammals and reptiles with increasing shares of farmland area . Such results are in line with previous studies reporting a link between higher shares of farmland area and species diversity through increased crop field sizes in more intensively managed and less heterogeneous landscapes e.g. see Martin et al. . Overall, our results suggest that the natural value of agricultural landscapes in our study area increases from the lowlands in the coast, towards mountain areas along a west-east gradient of decreasing agricultural intensity. Such results diverge from previous research by Olivero et al. and Gonz´ alez-García et al. , in which High Nature Value farmlands were associated with farming areas under higher agricultural management intensity . Such contrasting results seem related to the different approaches used, including the taxonomic groups considered, criteria used to select species and analytical approaches implemented. While an extended list of plants, reptiles, amphibians, birds and mammals of conservation interest showing a relation to agricultural landscapes were considered in this study, Olivero et al. included only species protected by Spanish legislation and species associated to agricultural landscapes, regardless of their conservation interest. Selection of indicator species in this study followed consolidated guidelines in the field of High Nature Value farmlands assessment , providing, in our view, a more accurate description of HNVf systems. Altogether, such differences, including the temporal mismatch of data reflecting farm-level management used, may explain the contrasting results, and highlight the importance of the selection of indicators and the definition of common approaches to assure comparability of assessments and monitoring in space and time. Still, our results are consistent with previous research obtained for the targeted region performed at the EU level e.g. see Paracchini et al. . While our results are promising,mobile vertical rack there is room for improvement and further research.

The mismatch between the spatial and temporal resolutions of farm-level management and species and habitats data hindered a fine-scale assessment of the impact of agricultural management on biodiversity patterns. In fact, the best available biodiversity data, a 10 × 10km presence-only dataset, do not include information on the abundance of species, which would be relevant to further scrutinize patterns of biodiversity across farming systems. The use of habitats and species protected under the different legal regulations at the European, Spanish and Galician level, is an asset for the work, since they allow an evaluation of the impact of agricultural management on the most vulnerable elements of biodiversity. In contrast, while being the best information available for the study area, biodiversity datasets target mainly species under any legal protection , are unbalanced for some taxonomic groups, often dominated by mobile species, thus providing a limited overview of the species richness across the taxonomic groups considered, and likely to impact the observed patterns. As an example, while expected, no significant relationship was found between plant richness and the distribution of different farming systems , fact that may reflect the lower number of plants in the dataset as being linked to the occurrence of agricultural landscapes.Further, while not within the scope of this research, habitat richness patterns could be scrutinized by focusing only on those known to be fully or partly dependent on agricultural practices, and by comparing patterns inside and outside Natura 2000 sites. While based on farm-level data and state-of-the-art approaches, results from our modelling should consider the dominance of specific farming systems across the study area. In fact, cattle-based farming systems and the resulting landscape types include most of the farms and thus prevail in the study area, potentially masking any significant link between other farming systems and biodiversity. Moreover, IACS dataset does not include other relevant information, such as N input, or the share of irrigated area that could be useful to characterize the identified FS from a management intensity perspective. Also, IACS/LPIS are provided after a process of anonymization, assuring that farmers’ identity and the geographical location of each agricultural plot are not disclosed, which limited our ability to account for spatial complexity in our assessment. Finally, while IACS dataset includes all farm holdings under CAP payments, it does not integrate information from other farmers and respective farm holdings, namely family farms, and part-time or retired farmers’, which may be reflected by the patterns observed at the landscape level. Farming systems are a complex component of food systems. They are primarily focused on food production. Farming systems consist of all the subcomponents that are pertinent to farm production, but they have blurred boundaries with other components of the food system and involve a wide range of stakeholders and players .

However, the decrease in overall effect size on biodiversity with increasing publication year may not be a reflection of research bias, and rather a reflection of real changes in functional diversity of non-domesticated taxa observed on the ground. On the one hand, the reduction in the positive effect of diversified farming systems on species richness across time may be directly related to the unprecedented biodiversity loss terrestrial systems experienced in recent decades . On the other hand, over the years, diversified farming systems may provide shelter and the necessary resources to support more abundance, especially given the increasing reduction and degradation of natural habitats in agricultural landscapes.Aquaculture production has increased rapidly in the last decade, with the highest production mass in 2018.Shellfish mollusks constitute a significant portion of aquaculture production, accounting for 56.3% of marine and coastal aquaculture production in 2018 . China is the most productive country , and the production of fed aquaculture in China has quintupled since 2000 . The razor clam, constricted tagelus , is usually cultured along the coast of Fujian and Zhejiang provinces in China, accounting for 4.9% of the world’s total mollusk production in 2018 . The tagelus is an in faunal bivalve that inhabits fine sand, silt, or sandy-mud sediments . It filters suspended particles by stretching the “water tube” within the sediments to the water column, which filters suspended organic matter through its “water tube” and regulates the cycles of nutrients via filtration, ingestion, and excretion . Although mollusk aquaculture has wide-ranging benefits, such as providing protein and solving food security issues, there are potential environmental concerns that should be tackled to follow with this rapidly growing sector to maintain its sustainability . One of the most important environmental concerns is emissions of greenhouse gases ; the magnitude, ebb and flow tray pathway, and controlling factors in shellfish mariculture are poorly constrained.

Generally, there are two pathways in which the bivalve may introduce GHG to the water column and subsequently to the atmosphere: the bivalve produces GHGs in the gut and shell biofilm mediated by syntrophic microbes , and the sedimentary releases GHGs due to the benthic mollusks’ regulation of nutrients and GHG cycling . The most debated gas is carbon dioxide ; the inclusion of CO2 produced during bivalve shell production should be considered in the carbon trading system . Studies have shown that the growth of shelled mollusks releases CO2 via respiration . However, other studies have suggested that shellfish cultivation results in a CO2 removal process because calcification can fix carbon in a solid form . Methane and nitrous oxide are two non-CO2 GHGs that have a global warming potential that is 28 and 273 times that of CO2 over a hundred-year lifespan . They were recently found to be released significantly in coastal “blue carbon” systems  and in aquaculture , which complicate GHG emission assessments . Thus, evaluating the potential of net GHG emissions from not only CO2 but also non-CO2 GHGs is reasonable. CH4 is mainly generated in a strictly anoxic environment mediated by microbes, known as methanogenesis , which is the last step in the degradation of organic matter. N2O is commonly produced in fresh and marine systems via microbial nitrification and denitrification . Bivalves were shown to induce a significant increase in CH4 and N2O efflux in marine sediments or in the anoxic gut, ascribed to microbial processes . In addition, dimethylsulfide is the most volatile sulfide in the oceans and is produced by the degradation of dimethylsulfoniopropionate . After being released into the air, DMS is oxidized to sulfate aerosols , an important driver that contributes partly to global warming . The global significance of GHG emissions from aquaculture ponds has been estimated , with large uncertainties due to poor-constrained consequences in emissions  and insufficient data. In addition, different farming systems may introduce large discrepancies in GHG emissions; therefore, appropriate strategies are necessary to minimize the GHG footprint.

For example, the fed crab pond was found to be a significant GHG source, with emission rates of 18.8 mmol CH4/m2 /d and 0.002 mmol N2O/m2 /d . Another feeding aquaculture system, the shrimp pond, was a GHG hot spot, with 0.02 mmol CO2/m2 /d, 0.01 mmol CH4/m2 /d, and 0.47 μmol N2O/m2 /d . Ebullition is likely the major pathway contributing to CH4 emissions in specific ponds, accounting for over 80% of the total CH4 flux . Nutrient loading into ponds due to regular feeding can increase the availability of organic matter, which may stimulate primary production and eventually lead to high GHG emissions . Moreover, the literature suggested that water drainage can transform ponds from a sink to a source of atmospheric CO2 and strengthen CH4 and N2O emissions , indicating the role of sediment in GHG emissions. Notably, GHG fluxes and the net global warming potential of multiple GHGs from shellfish mariculture remain largely uncertain, with a few studies reporting the measured rates . In this study, we conducted two field surveys in a constricted tagelus farming system comprising two ponds: a man-made microalgae-bloom pond for fodder and a tagelus culture pond for harvest. We tracked the CO2, CH4, N2O, and DMS concentrations; estimated the sea-airfluxes of GHGs at the interface of seawater; and quantified the total potential effect of GHG emissions in a routine cycle of water exchange in these two ponds to evaluate the role of the tagelus farming system in GHG cycles. Moreover, we measured the environmental parameters and identified the drivers of GHG emissions to understand the factors controlling GHG emissions and propose suggestions for the management of shellfish mariculture.In this study, we examined the site-specific fluxes of three GHGs from two mariculture ponds. We found that the strongly positive CH4 and N2O fluxes in the ponds offset the uptake of CO2 in the microalgae-culture ponds despite the sea-air interface fluxes of CO2 being higher than those of non-CO2 GHGs . Consequently, the constricted tagelus farming system was a net GHG source during the two sampling periods by converting the CH4 and N2O fluxes into CO2-equivalent units. In general, the net emissions in the shellfish tanks were two to three times higher than that in the microalgae tanks,4×8 flood tray corresponding to the high emission rates of CH4 and N2O in pond B. In pond A, N2O was the main contributor of the total radiative forcing, contributing 47–55% of the total radiative forcing.

Combining the two ponds, our field measurement showed that the total CO2-equivalent fluxes in the tagelus farming system were 135.25 mmol/m2 /day in March and 30.37 mmol/m2 /day in April and that the draining water accounted for 56–71% of the fluxes. By considering the fluxes in the ditch to represent the natural processes, we can estimate that the potential of total radiative forcing was 47–83% higher in the tagelus farming system during one routine period of water exchange compared with that in the natural environment. However, by excluding the process of draining wastewater, which may disturb the sediment surface and result in high GHG emissions , the total radiative forcing was, in turn, 4–21% lower in the tagelus farming system than in the natural environment. Another notable finding is that non-CO2 GHGs accounted for 57% and 94% of the total CO2-equivalent fluxes in March and April, respectively, which indicates that non-CO2 GHG emissions could exacerbate the warming potential in the constricted tagelus culture. Reasonable control of water drainage and farming modes may reduce GHG emissions and nutrient excess. Our estimation of GHG emissions may be underestimated because of the unconsidered effect of ebullition, which may vary in different farming ponds. For example, ebullition was recorded as the major pathway of CH4 emissions in shrimp ponds and crab ponds but may be a minor source of atmospheric CH4 in reservoirs deeper than 5 m . Considering the shallow water depth of our ponds and the manner in which the constricted tagelus lives , more fieldwork is necessary to evaluate the contribution of ebullition. Comparing our results with those of other studies may reflect the complexity of GHG emissions from shellfish aquaculture. For example, the most recent study showed that only limited GHGs were released in a commercial oyster farm in Rhode Island based on laboratory incubations and field sediment cores , which is different from our results . The differences in farming modes , quantification methods , and living habits or species may explain this discrepancy. Specifically, our results were obtained in constricted tagelus farming ponds that require fertilizer to stimulate their production and growth. GHG emissions were calculated from empirical models, which provide a net value after all physical and biochemical processes. In addition, any activity of tagelus disturbs the sediments and increases the possibility of the sedimentary release of GHG . By contrast, oysters have naturally evolved to live in dense populations and did not require the supplementation of cultivated food for growth . They were lying in the farming facility or held in plastic mesh bags above the sediment surface, which may have had less effect on the sedimentary release of GHG. Importantly, the GHG emission rate of the specific species was directly evaluated by laboratory incubations, and the sediment GHG flux was measured using chambers in the field . Other studies on shellfish farming have also found low sea-air CH4 flux beneath oyster aquaculture, with a maximum value of 0.1 mmol/ m2 /d , which was different from our case, in which non-CO2 GHGs contributed to the main radiative forcing.

Hence, farm management, where rich nutrient water inflows into the shellfish pond, wastewater drains to the ditch, and there is man-made algae bloom for feeding , was the predominant driver in our study, leading to significant GHG emissions . Another study showed that mussels can cause the concomitant enrichment of organic matter in sediments . Although it may influence GHG biogeochemical processes in the system and increase the rate of GHG release below the farming sediments, the farming-caused GHG emission was not significantly different from reference conditions . Therefore, GHG emissions in shellfish mariculture may complicate not only different aquaculture modes but also the spatial and temporal variations in farming species. A finding from our results that may benefit the climate is the emission of DMS from the microalgae ponds, particularly during strong radiative forcing periods. DMS is believed to be one of the most important precursors of sulfate aerosols, contributing to cloud condensation nuclei . Thus, the release of DMS may reduce the potential effects of GHG emissions from the tagelus farming system. However, directly comparing the GHG emission potential between DMS and GHGs is difficult because no standard protocols are available. Because the DMS flux was less than 1% of the total CO2-equivalent flux, we did not discuss the contribution of the cooling effect driven by the DMS to the estimation of the potential of total radiative forcing. However, our results raised our attention to the local importance of the role of DMS in man-made algae-bloom aquaculture. From the results of the correlations and PCA , we deduced that the concentration of DIC in the pond is the main factor that determines the CO2 flux. Thus, the balance between CO2 from photosynthesis and respiration may lead to changes in CO2 emissions. When the solar radiation was weak in March, the DIC and pCO2 were relatively higher in pond A in March than in April during the daytime , which indicates low photosynthesis and high respiration in March. In addition, during the night, in the absence of photosynthesis, a significant increase in DIC and pCO2 was observed in March and April . High productivity may be linked to a subsequent high respiration rate during the night, resulting in more CO2 release, causing an S-shaped curve, which is consistent with our results. Moreover, microalgae tanks contain lesser DIC and release lesser CO2 than that in shellfish tanks; therefore, microalgae tanks can absorb more CO2 from the atmosphere, which can act as a short-time sequestered carbon pool. By contrast, shellfish tanks were found to be responsible for over 95% of the total CO2 emissions, with a higher DIC concentration than that in microalgae ponds.

Differences in the type of production can have a significant implication for the use of CTF at its early development stage, at least. However, in the survey it was not specified on what types of production respondents apply CTF systems. In Table 3 results from a mean equality test for farm size between the CTF-user and non-user groups are presented. There is a significant difference in mean farm size between the two groups. The average farm area for the aggregate sample was 428 ha. CTF users operated significantly larger average farm area compared to 192 ha for Non-users . The farm numbers were too small to show statistical differences at a country level. About 77% of respondents reported that they were concerned about heavy machinery and its potential damage both on the field headlands and the main body of the field. Measures, other than direct adoption of CTF, being used by survey respondents to minimize traffic damage are presented in Table 4. Values in parenthesis are percentages relating to the CTF user only sub-sample. For the overall sample, the traffic damage minimization practices most used are: low ground pressure tyres on tractors and harvesters ; ploughing and sowing headlands last to reduce damage and; restricting grain trailers to field headlands for loading . The use of low-ground pressure tyres on tractors and harvesters is the most in use both in the overall sample and the CTF-user sub-sample. Deliberately fixing tramlines to minimize soil damage is moderately used when assessed for the total sample, but the second most used practice for CTF users. On the other hand, changing field turning headland to different parts and the use of dedicated crop transfer trailers fitted with large tyres are the least used.

When future adoption was considered, vertical rack system selection of smaller machines , selection of trailed equipment to reduce axle load and fixing tramlines are ranked highest. Half of the respondents use a combination of three or four of the nine damage minimization techniques listed in Table 4. CTF-users seem to use a combination of more techniques to minimize traffic-induced damage on their soil compared to the ’Non-users’ group where about 34% of the CTF-users and 23% of non-users apply a combination of five or more of the damage minimization techniques. Regarding crop establishment system, CTF-users employed reduced/ strip-tillage and no-till whereas the majority of Non-users practiced plough-based cultivation. There are wide differences in sample size, mean farm size and CTFuser proportion across countries included in the survey. Coupled with the sampling concerns of non-random selection, heterogeneity in sampling across countries, low survey response rate, and likely subjective/ perceptional differences in defining CTF, this makes it difficult to make cross country comparisons and/or generalizations at country level. As the available literature eliciting real experiences and perceptions of farmers is limited, the work reported here contributes to the development of this area of research.This is in line with the evidence that soil compaction is a threat to European agriculture . As shown in Table 4, farmers are employing a combination of techniques to minimize damage. CTF-users seem to use a greater number of damage minimization techniques in combination, probably because this group are acutely aware of compaction issues and open to adopting a multitude of measures. Among a list of techniques presented in Table 4, low-ground pressure tyres are the most used traffic damage minimization technique. Low ground pressure tyres have been recognized to improve topsoil conditions and crop yield . The technique is also the most in use by the CTF-user group. There can be several possible explanations for this. CTF has evolved from an approach where the pathways did not grow harvestable crop and where track widths were fixed, to a more flexible system where the base machine pathways are cropped, where some machine types have different track widths, and where varying machine weights require different tyre widths.

Consequently an approach where tyres and ground pressures are chosen to limit soil stress on traffic paths and field headlands is sensible. It is also possible that many who consider themselves as CTF-users but only limit or control traffic to a limited extent, also use low pressure tyres to limit damage generally. Overall, farmers that are most conscious of potential soil damage use a combination of techniques to protect the soil. CTF is used on larger farms with the CTF-user group on average operating nearly 5 times more area as the non-user group This may be a response to the need to reduce the damage risk of the heavier equipment on these farms coupled with the capacity of large farms to take advantage of economies of scale in machinery investment by amortizing fixed cost. This capacity to avail of scale benefits has been documented in previous studies including other PF technologies . In the current study, this is highlighted in the UK data where farm sizes are greater and CTF adoption is more common. The large mean farm size in UK is also docuemnted in Loughrey et al. . There are differences in the level of use of the system among the farmers who considered themselves as CTF-users, indicated by the proportion of farm area on which CTF is used. Most ‘CTF-users’ are not implementing a complete version of CTF as only 19% use the same pathways for all operations, and 56% use the same tracks/pathways for most crops and most machines. This has partly to do with subjectivities in definition of ‘CTF-user’ as described in the methods section. Partial adoption is to be expected as conversion to CTF demands several adjustments and learning through experimenting . Being conscious of the challenges of matching machine, track and tyre widths, and enabling deeper cultivation occasionally in full CTF systems, some farmers may still desire to manage their traffic by implementing some level of CTF as part of a soil management system that also includes reducing ground pressure and other soil protection measures. An important learning is that the current market for CTF rental/ contractor service appears to be very limited as most of the surveyed farmers practice CTF by their own. This could be because the technology and particularly its utility in terms of ’pay back’ is not fully proven across a range of real farm situations. Responses to open-ended questions about CTF challenges/disadvantages testify farmers’ challenges with contracting. Surveyed farmers have positive perceptions about the technical potential of CTF.

A considerable proportion of farmers who do not consider themselves as CTF-users said they would recommend other farmers to use CTF . This implies that implementation issues play a key role in adoption decisions. Overall, surveyed farmers had positive expectations about the benefits arising from using PF and CTF. Expectations about CTF adoption impact on long-term- gross margin were also optimistic. These expectations are supported by research on the effect of CTF adoption on crop yield and profit . Most of the surveyed farmers reported efficiency gains in machinery operating time due to CTF adoption. Optimistic expectations about labor saving and environmental benefits from using GNSS and PF were also expressed. There is already evidence of environmental benefits arising from practicing CTF in the form of reduced soil emissions . The long-term labor saving expectation is also supported by Luhaib et al. . The main issues farmers identified in this research regarding CTF were: affordability, compatibility; lack of decision support tools; adaptability ; and accessibility . Machinery cost is referred to as a pressing issue limiting farmers from taking advantage of CTF systems. This issue was also identified as a major constraint inAustralia . Perceptions that CTF is not suited for small farms are holding back European farmers from taking advantage of the technology, as was similarly noted by Larocque for western Canadian farmers. Examples from farmers’ responses to the open-ended questions about CTF challenges/disadvantages such as “cost of matching equipment”, “cost of machine renewal”, “cost of operations”, “big investments to get everything on the same track and working width, ”too expensive”, etc. exemplify the seriousness of the issue. Future availability of optimized turning paths and optimized field pathways to be implemented via auto-steer systems, in combination with low ground pressure and partially controlled traffic on headlands may be an alternative particularly for smaller farms. From the responses relating to the adoption of traffic damage limitation measures, traffic management measures that combine elements of CTF with other soil protection measures may have a role on many farms. If autonomous vehicles develop and take out the need for labour, machines may get smaller and their traffic issues would be more easily resolved with lower pressure tyres. Farmers in this study also emphasized the need for adaptable/flexible/simple machinery, compatibility among products from different suppliers, evidence of the utility of CTF systems under local conditions, decision support tools, and affordable purchase and modification solutions. Soil nematodes are of particular interest in soil food webs as they are the most abundant group of multicellular organisms in the soil . They occupy several trophic levels in the soil food web and can be classified into herbivores , bacterivores, fungivores, omnivores and predators . Additionally, they form complex networks with other soil biota, and play a crucial role in decomposition of soil organic matter, mineralization of plant nutrients and nutrient cycling . For instance, mobile grow rack nematodes regulate soil microbial communities and enhance microbial colonization through grazing on soil microbes . Being predators and prey, nematodes also provide information about the abundance and activity of other soil organisms, and thus have been used as indicators to study soil food web conditions , soil biodiversity and ecosystem functioning . Vegetable farming has become a major source of income for farmers worldwide.

Globally, vegetable fields account for approximately 7% of the total croplands, and this percentage is usually higher in developed countries . Compared to crop fields, vegetable fields are characterized by higher N application rates, more intensive production and management practices such as frequent irrigation and tillage as well as multiple planting-harvest cycles during the year . For example, fertilizer inputs in vegetable production were up to 600 kg N * ha− 1 *yr− 1 in comparison to 300 kg/ha of nitrogen per year in cereal cropping systems . Intensive agriculture has been shown to reduce soil biodiversity , which is of utmost importance for ecosystem functioning . Therefore, it is important to investigate how intensive farming practices may affect soil nematode communities. Organic farming systems are typically thought to be more sustainable than conventional systems , and organically managed farmlands have been growing to approximately 4.4 × 107 ha worldwide and are expected to increase further . This increasing trend is also true for organic vegetable farming in the Switzerland. Two global meta-analyses have shown that organic farming has a positive effect on soil biota , whereas a comprehensive understanding about whether and how organic management influences soil nematode community structure and associated functions is still lacking. Although the effects of organic farming on soil nematode have been assessed in grasslands , arable fields and vegetable fields , most studies are based on field experiments under homogeneous soil conditions at one particular location . The effect of organic farming on soil nematodes may vary with soil texture,crop species and land-use history . Thus, the effects of organic farming on soil nematode community may be dependent upon spatial scale because nematode abundance and community composition can be related to edaphic and climatic variations across scales . Finally, previous studies assessing the effect of organic farming on soil nematodes often focused on single vegetable types such as tomato , green peppers , and asparagus , and the comparative effect of organic farming on soil nematode communities across different vegetable types is largely unknown. Given the critical role of soil nematode in ecosystem functioning combined with increasing demands for organic vegetables, it is necessary to compare multiple field sites to obtain a robust assessment and a general understanding of how organic vegetable farming system influences soil nematodes. The wide-scale adoption of organic farming in Switzerland, particularly the Canton of Zurich with the second most licensees for organic products in 2020, provides a unique opportunity to elucidate how soil nematode communities and associated ecological processes respond to organic farming compared to conventional farming at a larger spatial scale.

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.