Category: hydroponic agriculture

Today, unlike the original gantry systems where one set of uncropped pathways received the same amount of traffic, a CTF managed field may have different pathways, some cropped and some uncropped, receiving different levels of traffic depending on the implement working widths, but all in multiples of the narrowest machine working width. CTF can also be viewed and implemented differently in different regions and/or across different farming groups with ‘seasonal CTF’ for example deploying a CTF system after primary cultivation until the end of that season or until the harvesting operation. The essence of CTF is to eliminate soil compaction within the cropped area, improve tractive efficiency on the permanent tracks, and thereby improve crop yield and economic return. Setting a CTF system on a farm is often made over years during which the machines being replaced are chosen, or they are modified, to match the CTF system chosen. Mainly a base working width has to be chosen . Fertilization and crop protection is often made at widths of 2, 3 or 4 times the primary working width. While CTF originally in Australia aimed to have the track width of all equipment the same, today in Europe it is often accepted that this is expensive, inconvenient and not suitable for road transport. Consequently a wider track width for combines is accepted. Besides, wider tyres are deployed to reduce the impact of traffic as all other traffic paths are cropped . Researchers have attempted to assess the economic and environmental benefits of CTF using field experiments. From a case study for a multi-cut grass silage system in Scotland, UK, Hargreaves et al. documented that introducing CTF provides a net economic return derived from increased yields due to a reduction in compaction and sward damage.

Antille et al. provided a review of the effects/implications of CTF systems on overall soil health, crop performance and yield,mobile vertical farm fertilizer and water use efficiency, and greenhouse gas emissions. As early as 1986, energy savings of approximately 50% were reported from CTF use in the Netherlands . In Denmark, Gasso et al. presented the significant potential for CTF to reduce environmental impacts through reduced greenhouse gas emissions in intensively managed arable cropping systems with at least 20% reduction in direct emissions from field operations. Based on a 10 year field experiment from Loess Plateau in China, Bai et al. indicated that CTF increased mean wheat yield by 11.2%. Drawing from a case study on an Australian sugar cane farm, Halpin et al. concluded that a farming system with precision CTF and minimum tillage is more profitable than traditional practice. Using whole farm modelling in Australian dryland agriculture, Kingwell, Fuchsbichler reported that CTF would increase profit by 50% mainly through its beneficial effect on yield and crop quality. Hussein et al. reported 30% increase in sorghum yield due to CTF. Studies from Denmark and the UK showed that CTF enables a considerable reduction in headland area and input use and claimed that the overall benefits would be higher if CTF was integrated with other precision farming techniques . CTF also provides other benefits such as minimizing soil runoff, economizing on input use from reduced overlaps, providing reduced operator stress with auto-steering and reducing soil-emissions . Tullberg documented that by restricting compaction to narrow and permanent wheel tracks, CTF contributes to reducing nitrous oxide emissions which are higher in compacted soils. Tullberg et al. concluded that CTF can bring about 30–50% reduction in soil nitrous oxide and methane emissions. The benefits can potentially be higher when CTF is combined with reduced tillage or no-tillage systems and assisted by precision agriculture technologies . While CTF is considered to provide multifaceted benefits as summarized above, there are also potential drawbacks associated with it. The main drawback is the investment required in suitable width matched machinery and the associated auto-steering technology. Driving patterns must be controlled, which can have implications for field efficiency in service vehicles like grain trailers or slurry tankers which must follow the pathways rather than turning to exit the field by the shortest distance when their load cycle is complete .

CTF is compatible with EU soil protection laws and regulations aimed at preventing soil compaction. While soil is compacted in the permanent track area 70–80% of the farm area is not compacted by field traffic where CTF is deployed . Low soil disturbance minimum tillage or no-till is more easily deployed with CTF as the soil is not subjected to traffic induced compaction. While the permanent tracks will be compacted, negative effects are limited to a small area and are more than compensated for by the lack of random traffic and intensive soil cultivation in the larger field area . While experimental evidence suggests multiple benefits from CTF, its use on commercial farms is limited for various reasons such as the high cost of machinery modification ; the perception that CTF is not for small farms ; and the lack of demonstrated benefits under local conditions. Moreover, CTF demands a change of mindset towards prioritizing soil health, careful route planning and making decisions with a long-term perspective and in a holistic manner. In Europe, soil compaction is already recognized as a threat . However, CTF remains a niche activity. In the literature, the benefits of CTF in terms of yield improvement, soil health, input-use efficiency and environmental benefits are frequently reported. However, literature on the perceptions/views, knowledge and concerns relating to CTF and its adoption, of current, and of potential, CTF using farmers, is lacking. This study intends to fill part of this gap by analyzing data from a survey of farmers, as part of adoption studies in two ICT-AGRI European projects: CTF-OptiMove and PAMCoBA . The primary objective is to assess and understand farmers’ perceptions about CTF and related technologies; what limits them from using the technology and how they think it could be improved. The study also seeks to identify intervention approaches, relevant stakeholders, and their roles for the future development of CTF. The data used in this study is from a cross sectional survey collected from January to April 2018 from 8 European countries using the network of the project participants to secure participants.The survey was a structured questionnaire administered online using the SurveyXact platform . An overview of the survey data is provided in Thomsen et al. . All 263 members of the CTF Europe association which includes farmers, advisors, machinery companies and others with an interest in CTF farming systems, were invited to participate.

CTF Europe member farmers generally operate larger farm sizes than average in their countries. In the Netherlands, the survey was distributed to 63 farmers, 3 were from the list in CTF Europe and the rest were members of a farmers’ association in the Hoeksche Waard district who cooperated in earlier projects on in-field traffic. Compared to other regions in the Netherlands, HW member farmers are considered more advanced and early adopters. In Belgium, the survey was distributed to approximately 2200 farmers using the sprayer inspection customer database for Flanders, administered by the Research Institute for Agriculture, Fisheries and Food . In Ireland, the survey was distributed to 140 farmers with active email addresses from the total membership of 200 of the Irish Tillage and Land Use Society . ILTUS members tend to be the larger growers in the country with between 100 and 800 ha per farm. A total of 103 valid survey responses were received and used in this study. The survey data included demographic attributes of the respondents , farm size, machinery ownership, tillage type, concern about soil damage due to heavy machinery and remedial measures, mode of farm ownership, perception/expectation about longterm benefits from using Precision Farming & GNSS and, use of CTF practices. Survey participants who considered themselves as ‘CTF-users’ were asked technical, experience and expectation related questions relating to their use of CTF. The survey questionnaire contained an introduction section giving background information about soil compaction and CTF. The conceptual definition of CTF provided in the introduction section was: “Controlled Traffic Farmingis a production and management system that requires the repeated use of the same wheel track for every operation, and for all vehicles and implements to have a particular span corresponding to the base wheel track”. In this study, ’CTF user’ denotes farmers’ own perception of their CTF use as responded to the question “Do you use CTF” . Two issues must be considered when analyzing the survey data. Firstly, the low response rate may introduce a selection bias, i.e., those farmers with prior experience with CTF technology and early adopters of mechanization technologies may have participated at a higher rate than those operating small farms and/or not considering CTF. Secondly, there is heterogeneity in sampling across countries in the survey. Members of CTF Europe already have awareness of and are interested in CTF.

The respondents from Ireland were members of a soil and tillage association that had participated in previous workshop events concerning soil compaction prevention, though not specifically CTF. However, the sample from Belgium is quite different because the criterion was owning a sprayer and only included the Flanders region with relatively small farm sizes. The study used a descriptive approach to present farmers’ perceptions, experiences, expectations, challenges and needs regarding CTF. Numerical data was summarized using percentages, cross-tabulations, vertical farming racks and histograms. Responses to open-ended questions were summarized and explained under thematic headings. Where it was considered useful, data was disaggregated by country and/or CTFuse category. Owing to the small sample size and sampling heterogeneity across the countries surveyed, the use of statistical analysis methods was limited. To assess the presence of statistically significant differences in mean farm size between CTF-user and non-user groups, a T-test was performed. 3. Results 3.1. Sample distribution, farm size and production type by country The distribution of survey respondents, farm size and production type is presented by country in Table 1. Most of the respondents were from Belgium, Ireland, the UK and the Netherlands. In terms of proportion of CTF-users, the UK sample ranks first followed by Ireland and Belgium . Because of their very small representation, samples from Germany, France, Canada and Sweden are grouped together as ’Others’. In Table 1 summary statistics for total farm size and the percentage of farm area where CTF is applied is presented . There is a wide difference in farm size across countries. Categorizing farm area into large , medium and small shows that nearly 86% of respondents from Belgium operate small farms in contrast to none for the UK sample. The majority of the respondents from Ireland and the Netherlands lie in the medium farm size category. Farm sizes are larger for the Danish and UK sample with 75% and 65% respectively greater than 500 ha. The percentage of farm area under CTF operations also differs across countries. The sample from Belgium is the lowest both in terms of farm size and the proportion under CTF practice. The UK sample features the highest values both in farm size and percentage area under CTF and this data is also from 14 CTF user farms, which is a much larger sample than from the other countries. As shown in Table 2, there appears to be considerable difference in the type of crop/animal production respondents are involved in . For the aggregate sample, 82% of respondents said to produce one or more cereal crops, 40% onion, 37% perennial crops and 31% beet with the least proportion involved in pig production. Note that a respondent could engage in more than one type of crop and/ or animal production. Cereal production is the most common for the sampled farmers with the vast majority reported to have produced one or more cereal crops . In UK and Ireland, all sampled farmers produce cereal crops. Pig production is the least common with only 50%, 16% and 6% of the samples from Denmark, Belgium and UK respectively involved in it.

The respondents were asked to select the two most important reasons for the conversion from a predefined list of 13 options ; one option also enabled an open response. The reasons listed in the question were obtained from previous studies and surveys. The marketing channels question addressed how their organic produce sales were divided into seven categories of channels : direct marketing, sales to primary production, sales to the processing sector, sales to the retail and wholesale trade, sales to private kitchens, sales to the public sector, and other channels. In addition, they were asked to provide their sales distribution divided as shares in their own region , the rest of Finland, and abroad. The results of the survey highlighted economic and environmental factors as significant drivers for converting from conventional to organic farming. The most important reason given by the farmers was smaller production costs leading to better viability, with 36% selecting this as their first option. The second most popular reason was ecology or sustainability, with 19% of farmers selecting this as their primary motivation. These two options were highlighted as significant in every region. Other reasons that were also frequently mentioned were healthiness and cleanness, a better price for their produce, subsidies, and the farm’s production already approximating organic farming practices. In addition, the survey revealed a wide variety of other reasons, from principles and ideology to specialisation and an interest in organic production. As anticipated, the results varied between the regions. One significant difference related to the importance of subsidies in the decision to convert from conventional to organic farming . Over 40% of the organic farmers in Kainuu stated that subsidies were among the two most important reasons for their farm conversion. In contrast, none of farmers from Satakunta selected this as an important option. It is interesting to note that Kainuu had the highest organic share and Satakunta had the lowest organic share. Indeed,roll bench the four regions where subsidies were given the highest importance were among the regions with the highest organic shares.

The development or availability of markets for organic products is also an important factor affecting farmers’ conversion decisions . The use of a broad range of marketing channels in a particular region indicates diverse demand and better sales opportunities. The results of the survey showed considerable variation in the utilised marketing channels . One of the regions used all seven categories, and in most of the regions, farmers sold their products to five or six of the marketing channels. The share of sales to the processing industry varied between 20% and 57%; primary production sales were between 25% and 57%; and direct sales varied between 0% and 20%. The proportion of sales to the retail and wholesale trade was the highest in Southwest Finland and the lowest in North Ostrobothnia.Other smaller markets included, for example, sales to abattoirs and sales through food collectives. According to the survey, the majority of the sales took place within the producer’s own region, 66% on average. In order to reveal regional differences in market concentration, the market concentration index was calculated for all the regions. A higher share was associated with more concentrated organic farmers’ markets in a region. The results also revealed that the least concentrated markets were located in some southern regions, such as Southwest Finland, H¨ ame, and Uusimaa . In contrast, the highest concentrations were found in Western Finland . For most of the conditions , we used the national average level to establish the position of demarcation between 0.33 and 0.67. This was a natural cut-off point to highlight cases below and above the average, as the studied cases covered all the mainland Finnish regions. The values 0.33 and 0.67 concern equal value ranges from the average national level. These value ranges were formed statistically: values of 0.67–1 and 0–0.33 were divided so that averages above or below 0.5 served as devisors. A value of 1 indicates that it is closest to the theory explaining the regional differences in organic farming. Fuzzy-set scores were set to the outcome and to all of the selected conditions in every case . The data indicated that a total of three regions have a clearly high organic share , while four are slightly above the average , four are slightly below the average , and the remaining four are clearly below the average .

The necessity analysis revealed that none of these conditions are necessary for a high organic share of total cultivated land when using a score of 0.90 as a consistency threshold for a necessary condition, a method similar to Marks et al. . Overall, the necessity analysis scores for consistency varied from 0.55 to 0.80 , with the highest scores associated with sectors as well as subsidies and the smallest markets. Our conceptual approach implies that different factors impact different regions; therefore, even the lowest score conditions were included in the sufficiency analysis. Table 5 presents the pathway results of the sufficiency analysis. The results showed three different pathways and covered five of the seven regions with a high organic share. None of the conditions are present in every pathway leading to a high organic share, which confirms that none of the conditions are necessary for a high organic share. The most common pathway to a high proportion of organic farming includes a long organic heritage, a concentration on dairy farming, and a region that places a high importance on subsidies. Pathway 1 represents the three highest organic shares in Finland . Pathway 2 differs from the first pathway in only one factor. Instead of a long heritage, it includes a larger farm size. Pathway 2 covers two regions, North Ostrobothnia and North Karelia. Pathway 3 is represented by one region . In pathway 3, a long organic heritage and larger farms and markets enable the high organic share. These pathways do not apply to two Finnish regions with higher organic shares, Pirkanmaa and Southeast Finland. In our results, consistency scores for all pathways are over the recommended 0.8. In two of the pathways, the consistency score is 1.00. The coverage scores are highest in pathways 1 and 2 . In the third pathway, the coverage score is 0.15. The solution score of 0.89 for solution consistency is over the threshold score of 0.75. Thus, the results can be considered sufficient to establish a set-theoretical relation. The solution score for coverage is 0.80, indicating that the three pathways apply to 80% of Finnish regions with an above-average organic share. The analyses for the low share organic farming regions confirmed the logic of the results for regions with a high organic share. One of the most common pathways to a low organic share was the mirror image of pathway 1: a lack of an organic heritage, a concentration on cereals production, and a low value placed on subsidies.

Overall, the analysis for the low organic regions revealed three different pathways with a solution coverage of 0.88 and a solution consistency of 0.88. These solutions cover all low organic regions as well as some high organic share regions. Thus, the absence of the selected conditions clearly reveals why some regions have a low proportion of organic land. This study reveals new knowledge about the regional differences in the share of organic cultivated land in mainland Finland. This kind of knowledge is needed to achieve the targets to increase organic farming and further promote rural development and a sustainability transition . In addition, our results are similar to those of Cairns et al. , as we show that QCA can be a valuable method for theory-testing regional studies that focus on complex entities. Our findings confirm the assertion of Ilbery et al. that the regional concentration of organic farming is explained by a combination of different factors rather than a single factor. However, our results suggest that the categorisation by Ilbery et al. should be supplemented with clear economic factors, such as the importance of subsidies, to improve coverage of the possible causes of regional concentrations in organic farming. The importance of subsidies has been highlighted in earlier studies ; however, previous research focused on farmers’ general decision-making rather than addressing the connection with regional differences. The location of farms in different Finnish regions affects their economic opportunities, and therefore the role of economic aid can vary. Our findings highlight the importance of economic aspects and align with the results of Lehtim¨ aki and Virtanen on the economisation of organic agriculture in Finland, at least to some extent. A close review of the data reveals that different types of regions utilise different pathways to achieve a high share of organic cultivated land. There are significant regional differences in cultivation conditions in Finland; therefore it is logical that the key factors involved in a high organic land share vary. Pathway 1 applies to the regions in Eastern Finland with the three highest shares of organic farming . This pathway confirms the results of Pietola and Lansik concerning low yields and subsidies, although the authors did not consider relevant educational or development programmes in the earlier decades of organic farming. Our findings suggest that a long organic heritage is one of the key factors affecting regional concentrations of organic farming, commercial greenhouse supplies a result also noted by Ilbery et al. . In comparison to other regions, a long regional organic heritage can represent an early social acceptance and learning from regional organic education actors or neighbours.

As L¨ ahdesm¨ aki et al. concluded, social acceptance is a key factor in achieving sustainability goals. In addition, increased knowledge helps to make the decision about the conversion . The absence of markets in this path may be due to the focus on dairy farming in these regions; dairy farming markets are often national rather than regional and rather concentrated. Although markets can still be important in these regions, they are not particularly versatile and may not be located locally. Overall, pathway 1 covers all the subsystems described in the food system conceptualization by Helenius et al. : socioeconomic subsystems , people as actors/decision-makers , and biophysical subsystems . Despite initially relating to the food system more generally, these three subsystems or categories seem to offer an apt categorisation of the different factors that are connected to the variation in regional organic farming. Pathway 2 describes the relevant factors in Northern Finland and also in one eastern Finnish region . The fact that both the first and second pathways apply to North Karelia reinforces its position as the region with the highest share of organic farming in Finland. In turn, pathway 3 illustrates the situation in Southern Finland , where markets seem to play an important, albeit not singular, role in the development of a high organic share. Our finding that markets are a significant factor aligns with the conclusions of several previous studies . The present study confirms several previous findings regarding the conditions in the regions with a high proportion of organic land. For example, as in other countries , lower agro-ecological conditions seem to play an important role in characterising the regions with the highest organic shares in Finland. However, agro-ecological conditions alone are insufficient to explain the high shares in these regions; instead, it appears to be the result of a combination of several conditions, as noted by Ilbery et al. . In addition, while markets seem to be important, market diversity and close proximity seem to be more relevant in regions that focus on cereals production. Moreover, the absence of these conditions seems to illustrate why some regions have a low share of organic land. Even though the unique characteristics of different countries and regions suggest that the pathways for Finland are not necessarily universally applicable, it is likely that similar factors and especially a combination of several conditions also affect regional differences and the share of organic land in areas outside Finland, particularly in middle-income and high-income countries.

Arid and semi-arid climatic conditions, low precipitation, poor soils, and overly intensive land use all pose the Land Use/Land Cover Change frequently change in the farming-pastoral ecotone of northern China and leading the complex landscape structure in this area . The farming-pastoral ecotone of north China is a transitional zone of China’s two biological communities: traditional pastoral and agricultural areas and is a valuable environmental security barrier zone , while Inner Mongolia is the central part of it. Due to its important ecological function, the LUCC in Inner Mongolia has received much attention for a long time, especially the land use types with valuable ecological service function like natural grassland, woodland and retired cropland which formulated by the “Grain for Green” program . GGP is by far the largest ecological restoration scheme and rural development program globally . GGP recommended the cropland has lower average grain yields to adopt artificial woodland, shrubbery since launched in Inner Mongolia in 2000 . GGP has accelerated the changes in land use in the ecological transition zone in the past 20 years, especially the changes in cropland use , and many studies have shown the strong ties between LUCC and GGP in Inner Mongolia since it has been in place to reduce deforestation, promote forest gain, and relieve human pressure on land through converting cropland to artificial planting with higher ecological service function.GGP has converted the cropland into artificial forest or woodland in southern China. At the same time, in the farming-pastoral ecotone in the northern foot of the Yinshan Mountains, due to the harsh ecological environment, shrub plants that are resistant to drought, cold and barrenness, were planted during the last two decades and have been shown a shrubland belt with distinctive characteristics . In addition, the complexity of cropland in the farming-pastoral ecotone in the northern foot of the Yinshan Mountains is not only reflected in the cropland-retired cropland conversation, but also there is a large amount of fallow and abandoned field in the preponement cropland, low round pots which can cause a range of social, economic, and environmental issues .

It has been 20 years since GGP started to be implemented in the northern farming-pastoral ecotone. Therefore, it is necessary to understand the effect of such ecological restoration project in ecologically fragile areas and the resulting land use changes. Using remote sensing data to map the cropland use change accurately is fundamental for evaluating ecosystem functions/services, policy formulation and implementation of agriculture in the ecological transition zone . Satellite remote sensing data have become an essential source of information for quantifying and better understanding environmental change, particularly monitoring vegetation dynamics from regional to global scale . Some studies have analyzed the vegetation dynamics changes in Inner Mongolia using long time series remote sensing data. For example, Hu and Nacun identified land use patterns and land cover change in Inner Mongolia from 1990 to 2015 using long-term remote sensing data. The result showed that the land use changes dramatically in Inner Mongolia and woodland increased the most. Also, they pointed out that most of the increased cropland was converted from grassland before 2000; the increased grassland area and improved vegetation coverage were the main land use process from 2000 to 2015. Li et al. used MODIS and Landsat dataset to map the land cover in Inner Mongolia in 2000 and 2014; according to their research, 35.3% of cropland converted to grassland, which the ecological restoration program could cause. Liu et al. found that grassland to cropland conversion and cropland retired as woodland or grassland co-occurred in the central farming-pastoral part of Inner Mongolia, while the grassland area generally decreased during 2000 to 2005. The conversion between cropland, grassland and cropland retirement in Inner Mongolia is highlighted in these studies. Other studies have treated the retired cropland as a separate land use type, and monitored and analyzed the temporal and spatial evolution of the retired cropland in Inner Mongolia. For example, Yin et al. used medium-resolution MODIS data to monitor the temporal and spatial changes of forest loss, forest gain and cropland retirement in Inner Mongolia from 2001 to 2014 when during the ecology restoration program implementation period. The results of this study showed that Inner Mongolia exhibited 1.32% of cropland-to-grassland conversions; besides, they pointed out that 0.29% of cropland has been converted to forest. However, the study did not note that the landscape of retired cropland is very different from forest and grassland in the farming-pastoral zone in central Inner Mongolia.

Other existent approaches which reported the retired cropland face insufficient to meet large-scale monitoring needs. The monitoring results of the previous county-scale or cityscale studies about the retired cropland in the farmingpastoral ecotone in the northern foot of Yinshan Mountains were revised by visual interpretation based on the supervision classification to achieve higher accuracy. Although the method mentioned above can interpret the land surface object and achieve good classification accuracy, but it is time-consuming and labor-intensive. It tends to show more prominent artificial interference. Therefore, it does not have the advantage of being promoted in a large area. These studies reported the presence of abandoned and fallow cropland at the northern foot of the Yinshan Mountains as well. Moreover, many non-cropped fields, including the fallow and abandonment, can easily misidentify with cropped and retired cropland and surrounding ecosystems using multi-spectral remote sensing images due to their natural regeneration . Hence, due to the complex and changeable cropland using types in the study area, remote sensing monitoring of land use in the farming-pastoral ecotone at the northern foot of the Yinshan Mountains has certain difficulties. However, there is a lack of research on monitoring retired cropland in a large area using high resolution remote sensing images. In recent years, remote sensing monitoring methods of land use change have evolved from local to the cloud computing platform and have continuously optimized and improved . Traditional data processing methods based on local software need to go through cumbersome steps such as data collection, data management, data preprocessing, and algorithm operation, which take up many local computing resources. The running time is often in days. In recent years, Google Earth Engine platform, which has gradually attracted attention, is a powerful platform for the analysis and presentation of “Remote Sensing Big Data” . Many classification methods are available for remote sensing image classification and have been ported to GEE. The Random Forest classifier is most widely used in LUCC monitoring because the Random Forest classifier can successfully handle high data dimensionality and multicollinearity with high classification accuracy both fast and insensitive to over-fitting . Therefore, GEE has an irreplaceable advantage in the long-term remote sensing classification of land use in a large area. Our research needs to process the long-term remote sensing data to identify the spatiotemporal change of cropland and the vegetation dynamics on a regional scale. GEE can well meet the needs of this research. We chose the northern foot of the Yinshan Mountains, located in the middle of the farming-pastoral ecotone in northern China, as the study area. In this study, we 1. employed a long-time series of Landsat archives and Random Forest classifier on the GEE to identify the cropland and retired cropland accurately for the four time notes; 2. adopted the Land Use Change Trajectory method to indicate the spatiotemporal characteristics of cropland and retired cropland change trajectories; 3. used a long-term vegetation index to reveal the relationship between vegetation dynamics and land use change in the study area and illustrate the GGP’ impact on the vegetation coverage.

The farming-pastoral ecotone in the northern foot of the Yinshan Mountains in Inner Mongolia is one of China’s three ecologically fragile zones . This area mainly distributes in northern arid and semi-arid grassland areas with annual precipitation of 300–450 mm and dryness of 1.0–2.0. The ecological environment’s fragility is expressed as arid climate, water resources shortage, loose soil structure, low vegetation coverage, and susceptibility to solid influences from wind erosion, water erosion, and human activities. Important ecosystem types of the farming-pastoral ecotone in the northern foot of the Yinshan Mountains include desert grassland, forest, sandy land, cropland, etc. Furthermore, ecological fragility and poverty are the two most prominent problems in this region, and the socio-economic development of this area is seriously affected by land degradation. Moreover, poor ecological conditions and the loss of a large amount of labor are the main reasons for the cropland instability that can cause the disorderly fallow, even cropland abandonment. Therefore, this phenomenon has seriously threatened agricultural production safety and has also become a representative and sensitive area for regional agricultural ecological security research . The farming-pastoral ecotone in the northern foot of the Yinshan Mountains contains 11 counties with a total area of 96,767km2 and spans nearly 730 km from east to west, with an average elevation of 1600 m. It presents a landform pattern dominated by low mountains and hills and wavy plateaus with less precipitation, heavy wind, a short frost-free period, and insufficient heat. Such climatic conditions have a significant impact on local agricultural development. On the other way, grassland pastoral areas mainly distribute in the northern part of the study area. In contrast, arid farming areas are mainly distributed in the southern part of the study area, as shown in Fig. 1.NDVI was calculated based on the normalized difference between the red and near-infrared bands; NDWI was calculated based on the normalized difference between the near infrared and green bands; NDISI was calculated based on the normalized difference between the thermal infrared , red, near-infrared bands and shortwave infrared 2. There is no middle infrared band in both Landsat-5 TM and Landsat-8 OLI images so that we used the shortwave infrared 2 band with the wavelength closest to the middle infrared wavelength to replace the MIR in formula . In this study, the separability of land use classes in the farming-pastoral ecotone in terms of spectral characteristics and index characteristics were discussed.We found that when the cropland contains both active cropland and non-active cropland , the separability among nonactive cropland, retired cropland, plastic pots 30 liters and natural grassland on the spectral bands and indices are weak . After the natural grassland is cultivated, the surface texture characteristics will be significantly changed to be substantially different from the natural grassland.

The retired cropland in the study area is mainly sparse shrubs, which is also different from cropland and natural grassland in texture. Therefore, we assume that texture metrics have the most significant contribution to identifying complex cropland uses. Texture metrics can provide valuable spatial information, reflecting the spatial distribution of the gray levels of remote sensing images and representing the spatial relationship between image features and the surrounding environment . Whether it is on the photograph, aerial photos, or satellite images, the texture is essential for identifying objects or regions of interest. GEE provides the GLCM gray level co-occurrence matrix function to calculate texture metrics. GLCM texture metrics have broad applicability and can be utilized in various image classification applications. Many studies have used texture features for land use classification . In this study, the 14 GLCM indicators proposed by Haralick et al. and 4 other indicators proposed by Conners et al. were used to construct texture metrics set. We modeled band-based texture metrics sets based on the Blue, Green, Red, NIR, SWIR1, SWIR2 of the Landsat5-TM TOA images. On the other hand, we only applied texture information based on the panchromatic band of Landsat-8 OLI TOA images. Comparing with the multi-spectral bands with a 30-meter resolution, the panchromatic band with a 15-meter resolution can provide more detailed texture information. The classification metrics were selected to construct a metrics set to recognize the cropland and the retired cropland in 1990, 2001, 2010, and 2019. The metrics set includes spectral metrics, index metrics, texture metrics, and terrain data . A reducer computes the specified percentiles was quoted to assemble time series data into multi-band image for machine learning classification on the GEE. After that, the texture metrics, elevation, and slope bands were added to the classification imagery.

Argyll and Bute is a county council area, on the West coast of Scotland, but also encompasses thirty three islands, twenty-three of which are inhabited . The physical geography of Argyll is made up of a large number of sea lochs intersecting the coast, resulting in a very long coastline. A large proportion of the population , live within 1km of the coast . Of the 32 Scottish local authorities Argyll and Bute is the second largest geographically, but has the third sparsest population . At the last census, the area had a total population of 88,100, with 25% of this population being over 65 . The area is subject to depopulation, with a population decline of 0.5% between 2018 and 2019. Like Lewis and Harris, the area historically suffered during the clearances , which impacted the traditional ways of life, but also impacted maritime activities such as fishing . Employment in Argyll and Bute is proportionally more reliant upon the physical environment , compared to the general Scottish population . Whilst fisheries were once an important sector, it now supplies less than 1% of employment in Argyll and Bute .The landscape is not only used in an extractive sense, 25 liter pot but is also vital as draw for tourism, where tourism businesses account for 13% of the share of businesses in Argyll, compared with 8% in the whole of Scotland .

The geographical area of Argyll and the Clyde is the largest producer of Atlantic salmon in Scotland and around 80% of Scotland’s pacific oysters, as well as 11% blue mussel production . In addition to the sea-loch and coastal net-pen sites, there are a number of freshwater hatchery sites located within Argyll and Bute. The fish farming industry is estimated to directly support around 460 employees. Like Lewis and Harris, disease episodes and escapes incidents from salmon farm sites have contributed to controversy around the fish farming industry, which have been covered by both local and national press .The process of grounded theory analysis begins as soon as the interviews are conducted . Once the interviews were transcribed, the process of coding began using MS Excel and following the protocol set out by Charmaz and Saldana . An example of which can be seen in Table 3. This technique allowed for codes to be written alongside the interview transcripts keeping them, and subsequent themes, close to the data. It is a suitable technique for working through rich qualitative data, as it assesses every line of interview text, helping the researcher to break it down and encouraging detailed exploration, generating new ideas . Alongside the initial line-by-line coding, memos were created where new ideas were developed from the data. Initial coding was applied across all the interviews first, and consecutively. After this was completed the second and third phases of coding began, where instead within the community, and gender. Participants came from a range of occupations, although there was a focus upon those involved in the marine environment. The following sections describe the themes that have been extracted from the interviews and are set out as subtitles. These themes represent a large portion of those that were extracted from the interviews but not all of them. Themes that were less prominent in the interviews have been excluded in the interests of focus and efficiency. The themes are presented according to the case study sites. However, there were a few themes that were found in both case studies. The two case studies have shown the complexity in experiences of the fish farming industry.

They showed the disjunction between the need both communities had for stable employment, and the perceived risk that fish farming posed to local places, which were highlighted as vital contributions to community identity. It also appears that the perceived identity of the fish farming industry plays into responses to the actions and consequences of fin-fish farming. This research has shown how the identity and place attachment could have consequences for social license to operate for the fish farming industry. The complexity of experiences was shown through the ways in which participants described their concerns around the growth of the fish farming industry and the increasing technological advances, framing these in a largely negative way. Conversely, there were elements of the fish farming industry that participants felt had positive impacts, the most important of which seemed to be the stable employment the industry provides. Reactions to new developments or changes to the environment are often rooted in place based attachment, making each development unique and complex . Further complexities are created through the proximity of communities to developments , as well as perceptions of the industries that are creating change . Therefore, the complexity of perceptions across the two case study sites is not unique. The community values that contributed to the identities of communities on Lewis and Harris and in Argyll and Bute, were perceived as juxtaposing with those that were perceived as important to the fish farming industry. Community values centred around systems which have local benefits, an appreciation of the marine environment and activities that “fit in” with the communities’ identity. The participants often described industry motivations as contrary to theirs. For the most part, these were expressed as the industry working hard to mask or make up for the negative impacts of the industry on local communities. Many interviewees focused on the idea that the industry’s largest motivator was profit. Alongside this the participants recognised the fish farming companies as multinational organisations. Participants had two contrasting perceptions, one of the older structure of the industry and one of the new. Looking at perceptions of the old style of industry, it was often described by participants as being local, emphasising its role in providing local jobs and producing a better product.

These results align with what has been seen in research around the SLO for aquaculture, where it was seen that local ownership allowed for greater integration into the community . The importance placed upon the farms being locally owned, could suggest that they were more associated with community identity and place, than the current system with the majority of production being done by multinational companies . In this sense it is possible to see that perhaps attachments to place and community identity can lead to the othering of the newer fish farming industry model. The transition from these smaller, local farms to the system now in place, where the majority of production is concentrated across five companies can be seen to feed into a sense of distance between the fish farming industries and communities. Multinational companies run the risk of becoming “place-less” as they lack being rooted in one place . Oftentimes companies working at these globalised scales, weaken the ties that they have within local spaces . This was emphasised by participants from Lewis and Harris in particular, as the multinational aspect of the fish farming companies operating in the local area and the rapid growth of sites was seen as the main cause of the problems that industry faces, both in terms of community acceptance and environmental impacts. This echoes what has been found by Baines and Edwards as they highlighted that a loss of social acceptability of aquaculture was in part because of a loss of connection to the industry, which then makes relationship and trust building harder to achieve . The results of this study have shown the influence that identity and place attachment can have upon perceptions of marine developments. Both cases studies provided evidence of communities’ strong attachment to the coastal and marine environment. For some, it was sharing stories of childhoods growing up on the coastlines of Lewis and Harris and for others it was describing the activities that they partake in with the local communities and marine environment. Sense of place plays an integral role in community identity, which can be seen in the way in which interviewees described their relationship with the coastlines in both sites . It is possible that places, and the meanings attributed to them, are so influential that they become one and the same, identity and place becoming intertwined . As Devine-Wright states, opposition to developments can be linked to perceptions that such development threaten identity, because it changes place, and therefore disrupts place attachment . Fin-fish farming activities do create change in landscapes so represent a potential disruption to place attachment . Such changes, and the potential environmental impacts of the fin-fish farming industry, were recognised by participants, suggesting that these risks are noticed by communities.

Therefore, seeing what appears to be a strong place attachment in the two communities of Lewis and Harris, and Argyll and Bute need to be taken into consideration when exploring the SLO for the fish farming industry. This is because what is likely to be acceptable to a community is dependent upon the socio-cultural norms of that community, within which place attachment and its role in identity, raspberry cultivation pot play a part. These socio cultural norms then bound what is acceptable, ultimately impacting SLO. Place attachment makes the social and contextual history of a place consequential for SLO. What is valued in place and attached to identity can be weighed against the risk posed by an activity, such as fin-fish farming. Ultimately affecting what is considered a legitimate and credible . There are further examples of ways in which identity and values can impact SLO. Differences in identity and what is ‘valued’ across the parties involved, have been shown to feed into conflicts . This is especially true where activities pose a risk to spaces valuable to communities . Conflicts can then have clear detrimental impacts upon SLO, especially on trust between those involved . Identity and values also influence response to information on both sides of relationships . Information is shaped by those who share it, with or within communities, as well as shaping industry responses to acceptability challenges . How information is utilised by communities and/or ENGOs has been shown to be influential upon SLO for marine activities . Finally, identity and values can impact trust between groups. The role of identity, especially the alignment of values between the two parties is highlighted as vital . Specifically, for blue economy activities, the role of values has also been recognised as a central component of SLO .The two case studies have shown the potential lack of trust caused by a perceived misalignment of values and identity. This can be seen in the way in which participants described what they saw as the values of the industry, focussing upon profit and production. This misalignment can add to the distance between the industry and communities, which is further exacerbated by the perceptions that decision-making is out-with of local areas. Together this could create a gulf between the two, ultimately making trust harder to achieve. Using a grounded and qualitative approach allowed for a finer scale analysis has shed light upon the complexities that make up SLO for the fin-fish farming industry. It has highlighted the influential role that community and industry identity can have upon SLO. Whilst the qualitative nature of this work means that it should be not be generalized outside of the study areas, the results reflect what has been shown in the wider literature, that SLO is especially influenced by the social and cultural context in which these relationships are attempting to be built . However, these results are also novel in revealing how a communities’ place attachment could influence how it perceives fin-fish farming companies. As well as showing how they can impact perceptions of industry identity. This matters as companies look to SLO frameworks when enacting community engagement. Measures used within the aquaculture industry with the aim to ultimately improve SLO, such as third party certification or community benefit schemes, may fail to improve SLO if they do little to improve the idea of the industry as “outsiders”. How the aquaculture industry could improve this is a question for further research, but also bears reflection as to whether these incompatibilities between industry and communities is something that can be fixed, or is a results of greater chasms between local communities identity experiences of multinational companies.

Accessible water volume and distance from surface water are important water sub-factors which influences on eucalyptus farming potential. Though eucalyptus species tolerate drought to different degrees, studies in southern Iran have demonstrated that reduction of water decreases tree diameters and heights . During periods of drought or in arid climates, irrigation is essential to support tree growth . Based on this research, annual volume of water accessible in most of the study area is sufficient for eucalyptus farming . Though water salinity is a limiting factor in eucalyptus growth, some species of eucalyptus are tolerant to salinity . Nearly fifty years ago, E. camaldulensis was recommended for cultivation in Khuzestan Province because of its tolerance to salinity . In most of study area, EC for surface water and groundwater is<4 ds/m and this makes these areas suitable for eucalyptus wood-farming . The climate sub-factors were less important than the other subfactors: annual wind speed mean was the most important sub-factor, and annual minimum temperature mean , annual temperature mean , annual relative humidity mean , and minimum temperature followed in order of importance. Annual wind speed mean is the most important factor limiting eucalyptus farming in Khuzestan Province. Eucalyptus is generally sensitive to higher wind speeds , but some species tolerate wind better than others. For example, the ratio of diameter to height is greater in E. microtheca than in E. camaldulensis,led grow lights making the former more resistant to high speed winds . The negative effect of certain wind characteristics on eucalyptus survival has been documented: warm wind decreased survival of eucalyptus seedlings in Fars Province .

Annual minimum temperature mean was second most important climate sub-factor. Eucalyptus is a tropical species and is vulnerable to cold . However, species of eucalyptuses tolerate temperature differently; as E. microtheca is less affected by minimum temperature than E. camaldulensis . Annual maximum temperature mean, maximum temperature, and annual rainfall mean are weighted equivalently and they were the least important determinants for eucalyptus farming in Khuzestan Province. There are shown to be suitable climatic conditions for eucalyptus wood-farming in Khuzestan province based on the results . And of the land cover sub-factors, non-planted sandy hills had the greatest impact of determining land suitability for eucalyptus wood-farming. Other studies have found that eucalyptus thrives in the sandy hills of the Albaji region near Ahvaz City in Khuzestan Province and similar results were found by others . Non-cultivated lands near the Jihad Nasr channels were the next land cover conditions of importance in eucalyptus wood-farming. Though these lands have not been evaluated before, water channels and drainage are suitable characteristics that enhance potential of these lands for eucalyptus plantations. The Jihad Nasr lands are also suitable for eucalyptus farming. Although, the Jihad Nasr lands are private at present, the wide and flat expanse of these lands, water channels around them and a proper drainage system that can decrease water salinity, makes them very suitable for eucalyptus farming. Eucalyptus plantations could replace other crops in these lands; this would only happen if it was economically justifiable and if eucalyptus seedlings were provided by the Forests, Rangelands, and Watershed Organization of Iran . Although, a large area of lands are recognized as suitable for eucalyptus farming , thepriority for wood-farming should be the large patches , because plantations on larger plots would be more economically viable operations. This research showed that accessible water in sufficient amounts, soil salinity, wind speed, and unplanted sandy hills are the sub-factors that most dictate land suitability for eucalyptus wood-farming in southern Iran.

Analysis of the wood-farming potential map based on FAHP showed that 16.8% of the study area is very suitable and 18% is suitable for eucalyptus wood-farming. Additionally, 16.55% of the study area is neither suitable nor unsuitable, 30.23% is unsuitable, and 18.42% is very unsuitable for eucalyptus wood-farming. More than 34% of the study area would be appropriate for eucalyptus wood-farming. This is confirms the hypothesis that Khuzestan Province has very good potential for eucalyptus wood-farming considering eucalyptus’ ecological, climatic, hydrologic, and edaphic needs. The tropical climate, large water supply, proper edaphic conditions especially in the northern half of the province, and existence of sandy lands makes this province ideal for eucalyptus farming. The results of other studies confirm this potential for the province . In the future studies, economic conditions should be considered for eucalyptus plantation in the province as well. The cost of maintaining eucalyptus seedlings during logging period should be assigned by FRWOI. For example, eucalyptus is sensitive to fire , therefore assigning a cost to monitor and protect eucalyptus plantation against future fires is very important , especially in Khuzestan Province. Validation of the wood-farming potential map showed an OA of 82% and a k of 0.71, based on the empirical data from successful eucalyptus plantations in Khuzestan Province. This demonstrates that the FAHP method has high validity for identifying the suitable lands for eucalyptus wood-farming and the wood-farming potential map is valid as well. Many studies have shown similarly high accuracies of FAHP method for environmental assessments . This study is the first to use the FAHP method to predict lands that would be most suitable for eucalyptus wood-farming; and it did so accurately. There are, however, some limitations regarding this approach. The lack of experts and specialists who answer to surveys may have created inconsistency in the results of this approach . In this case, if CR is more than 0.1, the survey procedures should be redone. Furthermore, a lack of experts on the subject is another limitation of this approach. An insufficient amount of data about successful eucalyptus plantations was an important challenge.

In this study, 80 polygons of successful eucalyptus plantation with a total area of 5002.45 ha provided the empirical data. These data were used to validate the wood-farming potential map. Eucalyptus plantations that were unsuccessful, however, were not included in this analysis and they were also removed from the eucalyptus plantation map. For more complete validation, more data are needed from sites of successful eucalyptus plantations in Khuzestan Province. Groundwater is an important water source for drinking, domestic and agricultural purposes particularly in rural areas. Meanwhile, the rural areas are faced with groundwater contamination due to various contamination sources including agricultural fertilizers, livestock manures, and domestic wastes . Recently, the livestock manures are paid attention to as a critical source of groundwater contamination as livestock farming has been expanded and intensified to meet the increased demand for meat . Livestock manures are spread on agricultural fields as fertilizers without composting or temporarily piled up in livestock farming fields without appropriate management until they are transported for treatment , which creates a livestock manure-derived groundwater plume in aquifers persistently affected by fertilizers. For instance, the amount of livestock manures increased from 71,530 tons a day in 1992 to 177,110 tons a day in 2012 in South Korea, and was estimated to be 185,069 tons a day in 2018 . Improper disposal of livestock manures has become a major cause of groundwater contamination in agro-livestock farming areas.Among the inorganic contaminants, nitrogen compounds are a major contaminant found in the LDGP at three forms , of which nitrate is the final product of nitrification and shows the widespread distribution in groundwater due to high mobility and low absorptivity . High levels of nitrate in groundwater are reported to cause diseases in humans such as methemoglobinemia and gastric and colorectal cancer in addition to ecological risks such as eutrophication and algal blooming in surface water . In addition, livestock manures are a source of pathogens such as bacteria, parasites and viruses ; many of those pathogens have been found to survive in the subsurface environment . Thus, careless management of livestock manures can induce pathogen-induced diseases including waterborne diseases on livestock and humans. In order to protect the rural groundwater quality from the livestock manure, it is essential to characterize the spatial and vertical extent of a LDGP. However, strawberry gutter system the evaluation of impacts by an individual pollution source in groundwater is challenging because various point and/or non-point sources complicatedly coexist .

For instance, major hydrochemical compositions including nitrate and SO4 2- come from both fertilizers and livestock manures and hydrochemically evolve in aquifers depending on redox conditions . Thus, recently the dual isotopes of nitrate are widely used to distinguish contamination sources , while they are expensive and time-consuming, and occasionally not applicable because of the isotopic overlap of nitrate sources . An integrated hydrochemical index can be used as an alternative tool to distinguish contamination sources as the river water quality indices and may reduce the uncertainties caused by the isotopic compositions. For the development of hydrochemical indices, hydrochemical indicators should be selected as in Solovey et al. who used Cl- /Br- to determine peatlands affected by anthropopressure and Ca2+/Mg2+ to determine the dominance of rainwater in a fen. Then the hydrochemical indicators should be coupled to provide a single index. Principal component analysis has been widely applied to assess major geochemical processes in aquifers and to choose hydrochemical indicators to address each geochemical process . When PCA is conducted using the isometric log-ratio transformed hydrochemical parameters, the ilr coordinates of a subcomposition can be recommended as a method to integrate the selected ions . This study was conducted to select hydrochemical indicators through the understanding of the hydrochemistry of a LDGP and geochemical processes occurring within the LDGP, and to suggest a hydrochemical index effectively differentiating the LDGP from the pervasive agricultural contamination in shallow unconsolidated aquifers in agro-livestock farming areas. For this purpose, multilevel monitoring wells were installed at both upgradient and downgradient of feedlots and manure piles, and then the hydrochemistry, dual isotopic composition of nitrate, and fecal microorganisms in groundwater with depth were examined. Based on the study result, hydrochemical parameters that differentiated the LDGP were chosen and combined to suggest an integrated hydrochemical index to trace the LDGP in a shallow unconfined aquifer. The applicability of the index was validated using hydrochemical and isotopic compositions from three other agro-livestock farming areas in South Korea. The methodology to select hydrochemical parameters for distinguishing a LDGP and to combine them to develop a hydrochemical index and the biogeochemical processes within the LDGP found in this study will be useful for managing groundwater pollution by livestock manures in agro-livestock farming areas. This study was conducted in an agro-livestock farming area in the Chungnam Province of South Korea , in which the lowland began to be converted to livestock farms from agricultural fields approximately in 1995, and the areal extent of livestock farming was progressively expanded at the down gradient area .

As of 2010, the main land uses included livestock farming and agricultural fields as in Fig. 1a, and livestock farms were located around agricultural fields. The geology consisted of Jurassic biotite granite, which was locally covered with coarse- to medium-grained sand . Colluvium materials such as rock debris, gravel, silt and sand were also observed when MLWs were installed in 2013 . The land surface had elevations in a range of 30 to 50 m above sea level and tended to slope gradually towards the east . In the agricultural fields, which were developed in an upland at elevations of 44 ~ 50 m asl, crops such as peppers, sweet potatoes and beans were cultivated with the application of fertilizers including silicate fertilizers. It should be noted that the application of fertilizers must have occurred all over the study area before 1995 and probably more intensively given that the overuse of fertilizers resulted in the acidification and nitrate contamination of shallow groundwater in South Korea . The use of chemical fertilizers has been regulated since 2004, decreasing the use of chemical fertilizers down to 46% between 1994 and 2014, whereas the use of livestock manures increased by 30% during the same period in South Korea . In the livestock farming area located in a lowland at elevations of 30 ~ 43 m asl, livestock manures were estimated to be produced at a rate of 5,090 kg day− 1 as of 2010 based on a total of 135 dairy cows and average N produced by a dairy cow .

The method uses the concepts of critical and interacting thresholds to challenge stakeholders in a workshop setting to think about potential non-linear and undesired behavior of their farming system. Following, stakeholders are elicited on desired alternative systems that avoid critical thresholds and thus improve sustainability and resilience . The method is flexible regarding: a. the information sources used as input for the workshop, b. the possibility to include case specific indicators and c. the stakeholder input during the workshop, i.e. alternation of individual input, small group discussions and plenary discussions. We illustrate the usefulness of the approach with an application to the extensive sheep farming system in Huesca, Spain. In this farming system, ongoing, interrelated economic, social as well as environmental developments are increasingly reducing the system’s sustainability and resilience. The proposed methodology presented in this paper extends the Framework of Participatory Impact Assessment for Sustainable and Resilient EU farming systems approach for assessing sustainability and resilience of current systems with participatory assessments on resilience of EU farming systems in the future . FoPIASURE-Farm 1 and 2 are based on the SURE-Farm resilience framework : 1) defining and delineating the farming system, 2) identifying main challenges, 3) assessing farming system functions, 4) assessing the system’s resilience capacities , and 5) assessing the system’s resilience enhancing attributes . While FoPIA-SURE-Farm 1 was mainly aimed at performance levels of main indicators, that represent main functions of the system, and resilience attributes, FoPIA-SURE-Farm 2 includes resilience concepts such as critical thresholds, interactions between thresholds , nft channel and regime shifts . In this paper we define the basis of a farming system as the farms producing the main products of interest in a regional context. Farming system actors included in the farming systems are the producers of main products and other actors that mutually influence one another.

The perceived complementarity of sustainability and resilience is operationalized by distinguishing system challenges, function indicators and resilience attributes. In the context of resilience, challenges relate to the question “resilience to what?”, such as resilience to weather extremes . Function indicators are case-study specific representatives for important system functions, such as “Food production” or “Maintaining natural resources”, as direct metrics for those functions are often not available . In the context of resilience, function indicators relate to the question “resilience for what?”. This relates to sustainability, which is defined as an adequate performance of all system functions across the environmental, economic and social domain . Resilience attributes are characteristics that convey general resilience to a system . These resilience attributes can often be linked to system resources , e.g. natural or social capital, that can only be maintained when system functions are performing adequately. To improve the flexibility of the methodology and the clarity and saliency of participatory input, just like for functions, case-study specific indicators may be used for resilience attributes, as well as for challenges. Based on workshop results, inductions are made about the resilience capacities of the studied farming system . For more details on the concepts used in this study, see Appendix A. FoPIA-SURE-Farm 2 consists of a preparation phase, a participatory workshop and an evaluation phase, and was developed for application and comparison across 11 EU farming systems . In this paper we present six key steps of the methodology . In Step 1, current performance and trends of function indicators and resilience attributes are assessed by the research team in the preparation phase. This assessment can be largely based on FoPIA-SURE-Farm 1 , but other literature can also be used. In Step 2, critical thresholds of important system challenges, function indicators and resilience attributes are assessed by workshop participants. Based on Biggs et al. and Kinzig et al. , we define critical thresholds as the levels at which challenges, function indicators or resilience attributes are expected to cause large and permanent system change. System’s closeness to thresholds is consequently evaluated by the research team based on participants’ comments and literature, e.g. based on ongoing trends identified in Step 1. In Step 3, performance of main function indicators and resilience attributes is assessed when critical thresholds of main challenges would be exceeded.

Possibilities of interacting thresholds can be discussed during the workshop and in the evaluation phase, following the framework of Kinzig et al. . Interacting thresholds are thresholds, that, when exceeded, lead to the exceedance of another threshold, i.e. there are cascading effects. In summary, Step 1, 2 and 3 provide an overview of possible system performance in case no adaptations for improved sustainability and resilience are made. Keeping the sustainability and resilience of the current system and the impact of exceeding critical thresholds as a point of reference, Step 4 addresses possible desired changes of the farming system towards the future. Participants can indicate and discuss what alternative systems are possible when challenges would become more severe, and when/how certain function indicators and resilience attributes would improve compared to the current system configuration. Step 5 aims to gain information on the strategies that are needed to realize alternative systems. We indicate these strategies as “future strategies”. Steps 2 to 5 correspond largely to the participatory workshop phase. In the workshop, individual, break-out and plenary sessions are alternated. Individual and break-out sessions are included to ensure that all participants can provide input, which can be used as input for further discussions in plenary sessions. The proposed session format in each step can be changed according to needs of the participants, as long as a balance between individual, break-out and plenary sessions is maintained. In Step 6, in the evaluation phase, researchers evaluate whether desired future systems, i.e. the current system maintained in the future and the alternative systems, are compatible with developments in Shared Social Pathways for European agriculture and hence match exogenous developments at European level. The time horizon for the future is 2030 in all steps. In the next sections we present details of each of the six steps. A pre-selection is made of most important system function indicators and resilience attributes, their qualitative description of performance and developments . Step 1 can be based on FoPIA-SURE-Farm and/or other information sources. Participants individually evaluate the existence of critical thresholds related to function indicators, resilience attributes and challenges . Walker and Salt mention that it is impossible to determine critical thresholds for resilience attributes because they all interact. However, we include resilience attributes as it stimulates thinking about resilience. Moreover, participatory input on thresholds can be interpreted as formulations of potential concerns for which management goals and strategies may be developed . In plenary sessions, individual input is discussed.

Participants are free to discuss and conclude on the relative closeness of their system to critical thresholds. In case closeness of the system to critical thresholds is not indicated by participants, the research team evaluates closeness based on the current performance levels, and magnitude of variation and/or trends. “Not close”, “somewhat close” and “close” to thresholds are defined as respectively unlikely, somewhat likely and likely that the distance to critical thresholds will be trespassed in the coming ten years, based on knowledge on possible variation and/or trends. A fourth category is identified as current levels being already at or beyond the critical threshold . Per identified main challenge, it is evaluated in a participatory forecasting approach what the effect of a change beyond the indicated thresholds would be on main indicators and resilience attributes . For this, the group is split in small groups of participants, each discussing one challenge. First, the expected direction of change of the challenge is clarified. Secondly, the relation between challenge and function indicator or resilience attribute is discussed. In each group, a moderator synthesizes this with a score of –, -, +-, + and ++ alongside arrows from challenges to function indicators and resilience attributes. A + relation implies that if the level of the challenge increases, the function indicator or resilience attribute also increases . Verifications are also made in relation to possible interactions among and between function indicators and resilience attributes. Optionally, the expected impact on the function indicator or resilience attribute is indicated. This impact is scored referring to the expected performance level from 1 to 5, similar to FoPIA-SURE-Farm 1 . In a plenary session, each moderator feeds back the results of the small group in a 1-minute pitch, after which participants can respond. Based on the outcome of questions on critical thresholds and forecasting the impacts of exceeding them, the possibility of interacting critical thresholds is evaluated by researchers in the evaluation phase using the framework of Kinzig et al. . Kinzig et al. specifically assess critical thresholds and cascading effects across scales for alternative future states of agricultural regions. Kinzig et al. distinguish the ecological, as well as the economic and social/cultural domain across the patch, farm and region scale. A good balance between developments in the different domains and levels may improve sustainability and resilience of a system . In systems with strong interactions between system variables at lower levels, vulnerability of the system at the focal level may increase . This is especially the case when variables at lower levels are all aligned with regard to their closeness to critical thresholds . An simultaneous exceedance of critical thresholds at lower levels may result in further cascading effects and ultimately result in an alternative, undesired system state at focal level, hydroponic nft which in this study is the farming system. In the context of this paper we distinguish the environmental, economic and social domains and the field, farm and farming system levels.

In a forecasting approach for improved sustainability, results are largely based on dominant trends and causal mechanisms that often lead to low sustainability. Solutions for improved sustainability, therefore, ideally need to break these trends and causal mechanisms . In this part of the workshop, we therefore shift from a forecasting approach to a back casting approach. A back casting approach has greater problem-solving capacities in long term challenges, because it is concerned less with what is likely to happen and more with what is desirable in the future . Picturing future systems may stimulate system actors to widen their perspectives and improve their understanding of the concept of sustainability . In this study, the back casting approach is focused on alternative farming systems that have improved performance of function indicators and resilience attributes . To identify these alternative systems, all participants are asked to write on post-its alternative systems they desire if challenges cross thresholds and/or functions need improvement. This ensures that stakeholders can give their own input and are not directly influenced by others. If input is low, thinking can be stimulated among participants by presenting alternative systems that are identified by the research team in the preparation phase. Based on the post-its, several alternative future systems are identified in a plenary session. These alternative systems may be combinations of suggestions of different participants. Some may be adaptations and some transformations of the current system. After giving them a name, per alternative system, one small group of participants is formed to further discuss which main function indicators and resilience attributes will change. In addition, changes in land use, sectors, objectives and other relevant aspects may be discussed. Participants in small groups also discuss the enabling conditions, i.e. how challenges and other drivers should change in order to be able to reach these alternative systems. Small groups consist of at least one moderator from the research team and three participants. In the evaluation phase, enabling conditions are categorized by researchers under the following domains: agronomic, economic, environmental, institutional, social. Taking alternative systems as the points of reference, the back casting approach is continued by identifying strategies to realize the alternative systems, in the small groups. A strategy is seen and communicated to workshop participants as a “plan of action, or part of it, implemented by actors within and outside the farming system to maintain or reach a desired farming system in 2030”.

The possibility of trading soil carbon credits has also been studied, and credits for carbon sequestration by agriculture have begun to be traded in voluntary markets . However, there currently are no reports on the market size of soil credits or credit prices. The potential of oceanic blue carbon as a source of credits through protection in offshore areas has been highlighted recently, but there is currently a lack of scientific knowledge and policy experience on this topic . Hutto et al.discusses phytoplankton, kelp, fish, and whales as oceanic blue carbon . The role of carbon removal and storage in the transport of kelp and phytoplankton biomass to deep-sea sediments and in the dead fall of fish and whales to the deep sea has been become increasingly recognized. In turn, carbon accumulated in the upper layers of deep-sea sediments may be released into the atmosphere when they are disturbed by bottom-trawl fishing.Preventing the loss of marine blue carbon through trawling by establishing marine protected areas and increasing the amount of marine blue carbon deposited by increasing the number of fish and whales could lead to the creation of blue carbon credits. Several voluntary carbon markets have certified blue carbon offset methodologies and implementation protocols. These markets are almost all for mangroves and salt marshes. To the best of our knowledge, there is no voluntary market for seagrass meadows, macroalgal beds, and macroalgae farming, although their CO2 uptake potentials are large. Here, we review three blue carbon offset credit projects being implemented in Japan in seagrass meadows,ebb and flow tray including the world’s first three projects that incorporate macroalgal beds and macroalgae farming.

Specifically, the blue carbon offset credit projects include: the project in Yokohama City, the world’s first; the project in Fukuoka City, the second such project in Japan; and the first Japanese national governmental demonstration project. Then, we show the challenges encountered in implementing these projects in terms of people, goods, money, and mechanisms, and how the problems were solved. Finally, we discuss issues and directions for future project expansion. The socioeconomic aims of blue carbon initiatives include improving the capital value and economic benefits of SCEs, improving their cost effectiveness as public works, and promoting local business. The economic benefits include economic incentives, including carbon offset credits , payments for ecosystem services , and income from funds. Historically, carbon offset credits have been implemented using a top-down approach. Here, international markets are first established, and credit markets at the national and local government levels are subsequently created. However, in the new framework adopted at COP21 in 2015, which is legally binding after 2020 as part of the Paris Agreement, mitigation measures are undertaken in a unique way by each country, and the basic policy includes a mutual verification mechanism . Thus, to implement the new framework of the Paris Agreement, both global and local climate change countermeasures will be promoted. In addition, the use of monetary incentives to appeal to the private sector requires a bottom-up approach in which markets are newly established at the spatial scale of local governments and privately led projects are developed. For the social implementation of carbon credit schemes, independent methods for the measurement, reporting, and verification of credits are needed. These methods involve accurate, objective, and quantitative measurements of carbon based on scientific and technological knowledge, transparent reporting, and verification.

The submission of greenhouse gas inventories to the UNFCCC Convention Secretariat is based on the MRV principle. Mitigation of climate change by storing atmospheric CO2 in the sea via natural systems can be achieved by three approaches: creating new target ecosystems ; reducing the declineof target ecosystems through restoration and conservation; and improving the management of target environments and ecosystems . Various guidelines for measuring carbon storage and CO2 uptake by blue carbon ecosystems and for creating credits for blue carbon have been developed. Australia has included blue carbon ecosystems in its national greenhouse gas accounts. The Australian Government’s Emissions Reduction Fund has developed comprehensive guidelines for that purpose. Other organizations that have produced guidelines include the IPCC , Conservation International, UNESCO, the International Union for Conservation of Nature, UNEP and the Center for International Forestry Research, and the Verified Carbon Standard , which is an independent carbon trading certification body in the United States. In Japan, guidance documents describing measurement methods for seagrass meadows, tidal flats, embayments, and port facilities have been prepared. The voluntary market credit system is operated and managed mainly by the US and Europe, with rules created by Verra , Gold Standard , and Plan Vivo . Plan Vivo, for example, has created the world’s first community-based blue carbon credit for the conservation and regeneration of mangrove forests in the Gazi region of Kenya. The project, Mikoko Pamoja, includes the Kenya National Marine Fisheries Research Institute and British and American organizations as actors and funders. However, one challenge remains—Plan Vivo’s methodology does not include sediment, which is a major carbon reservoir. Verra, formerly known as the Verified Carbon Standard , has been working to develop methodologies for blue carbon ecosystems. In 2015, it published a methodology that can be adapted to the restoration of seagrass beds and salt marshes.

In September 2020, Verra extended the methodology to the conservation of wetlands . VM0007 has been used to register the world’s first project on the conservation of mangrove ecosystems, including sediments, in Cispat´ a, in the Gulf of Morroquillo, Colombia; the project is supported by Conservation International and Apple. In May 2021, Apple purchased 17,000 tonnes of CO2 equivalents to offset its comprehensive carbon footprint for fiscal year 2020. In Pakistan’s Sindh Province, a 60- year conservation and regeneration project for 350,000 ha of mangrove forests has also applied to offer offsets and is currently being verified by Verra. However, projects targeting seagrass beds and salt marshes using VM0033 have not been registered to date. In the following section, we review three blue carbon offset credit projects for seagrass meadows, macroalgal beds, and macroalgae farming in Japan . Overall, members of the Japanese public are supportive of blue carbon projects. One possible reason for this support is that various related entities participate in the conservation and restoration projects to generate carbon offset credits; hence, the credit buyers may be more sympathetic to the projects as a whole, rather than just the carbon credits themselves. There are many stakeholders, such as managers, users, and implementers of conservation activities, in the same marine areas. Conflicts can arise, for example, between participants in marine leisure and conservation activities, but mediation between stakeholders by municipalities and other groups such as the Hakata Bay NEXT Conference may be a factor in the success of these blue carbon projects. Nevertheless, to realize a successful project, it is necessary to manage and invest human, material, and financial resources under an appropriate system or mechanism. Therefore, we extracted and compared these elements for each project. In March 2011, Yokohama City formulated the “Yokohama City Action Plan for Global Warming Countermeasures” based on the Yokohama City Ordinance on the Conservation of Living Environment. As part of the global warming countermeasure projects in this plan, the city has been working on using its own certified credits through the Yokohama Blue Carbon Project. Even though scientific knowledge on blue carbon is scarce and social implementation has been slow, Yokohama City was the first entity in the world to establish its own system and promote measures against global warming in the sea area. Meanwhile, Fukuoka City formulated the Hakata Bay Environmental Conservation Plan in January 2008 with the aim of conserving water quality and promoting the conservation, regeneration, and creation of the rich natural environment of Hakata Bay. In 2016, the second plan was formulated with the objectives of preserving a habitat where abundant macroalgae and seagrasses grow, expanding their growing areas, and providing habitat where young fish can grow. The Japanese national government has established the CIP system of government-approved private corporations and related laws to promote collaboration among industry, government, academia, and the private sector. As explained in Section 3.3, JBE utilizes this CIP system and works with companies, local governments, NGOs, NPOs, and other organizations to promote research and study in an environment fostering cross-industrial cooperation. Human resources are critically important to the success of these projects. In the case of the Yokohama Blue Carbon Project, the following can be considered as success factors with regard to the people involved. First, Yokohama City, as a model city, had already been implementing a wide variety of measures to combat global warming in coastal areas through partnerships among industry, 4×8 flood tray government, academia, and private organizations.

Its citizens have developed a sense of identity and civic pride in the sea through the promotion of the “Ocean City Yokohama” policy. Successive Yokohama City officials have been enthusiastic about the project. As a result of all these factors, Yokohama City was able to pioneer its own scheme ahead of the rest of the world despite having incomplete scientific knowledge about blue carbon and a general lack of social implementation. Importantly, the very positive attitude of both credit creators and credit users toward the environment matched the purpose of this project. In the case of Fukuoka City, various entities, including private citizens, citizen groups, fishers, businesses, educators, and the government, have successfully worked together toward the conservation and creation of marine ecosystems. A foundation had been laid for cooperation among industry, government, academic, and private-sector entities. Furthermore, the Hakata Bay NEXT Conference was established to promote collaboration among these various stakeholders. JBE has been led by people who have supported local government initiatives. Notably, the representatives of the Japanese national government have been also hosting blue carbon study groups and discussion committees for several years. In addition, the JBE supports collaborative work with companies, local governments, NGOs, NPOs, and other organizations to promote research and study.In the case of Yokohama City, eelgrass restoration was conducted at Sea Park Yokohama. The Sea Park is a sandy beach artificially created by the City of Yokohama in 1988; it is owned by the City of Yokohama. Eelgrass restoration activities started here in about 2001, and the continued efforts led to the recovery of the eelgrass beds. The fact that the sea area within the Hakkeijima Sea Paradise in Yokohama could be used as a field for a demonstration experiment of macroalgae farming was another important factor in the project’s success. In the case of Fukuoka City, the formation of a place where citizens could familiarize themselves with the water was planned around 1989 as part of the port administration. Against this background, the maintenance and management of natural eelgrass beds and the creation of macroalgal beds on bio-symbiotic blue infrastructure have been implemented in the waters of Hakata Port. In the case of JBE, a sea area in the Port of Yokohama was selected as the first demonstration site for the J-Blue Credit. Restoration of eelgrass beds has been carried out in this sea area since 2013. In addition, a macroalgal bed creation experiment conducted by the government from2010 to 2012 resulted in the formation of a Sargassum bed, and fishers are now harvesting while managing the resource. Financing is critically important for any project. In the case of Yokohama City, the project was selected as a CNCA Innovation Fund project and was able to proceed using foreign funding. Over time, additional funding was secured from Yokohama City . Furthermore, the sale of credits generated income for the project implementer. In the case of Fukuoka City, financial resources were secured through the creation of a new funding scheme that utilized port charges. With the establishment of the blue carbon offset system, a framework was created through which companies can purchase eelgrass as part of their CSR activities or ESG management, thereby financially supporting the activities of the Hakata Bay NEXT Conference. Costs for the founding of the JBE and the national demonstration project were minimized by streamlining administrative procedures and personnel.

One explanation could be that the higher likelihood for influence outweighed the effect of status, since relatively lower status farmers could still influence an entrepreneurial attitude towards organic. The status mechanism in the high threshold scenario can, therefore, explain why, e.g., entrepreneurs enter the organic market, and/or why homophily in farming styles in markets develop. An interesting result of the model, is that new entrants are important for the diffusion of organic farming. The influence of new entrants in this research went via two factors: their farming styles and their peer influence. A higher chance that the successor had the same farming style as his/her predecessor led to fewer organic farmers and organic pigs, irrespective of the parametrisation of the social influence mechanism in the model. This result was confirmed by the experts. Moreover, it is in line with previous research findings that associated organic farmers more often with an urban background , assuming that farmers with an urban background have a different farming style through different peers. It is also in line with the social identity theory , assuming that successors have a higher chance for different farming styles than their predecessors through different reference groups. More research in the farming styles of successors, including the diversity of farming styles and development of new rationales, would be interesting for gaining insight in diffusion of alternative farming practices. It should be noted that this result also means that when farmers have a smaller chance to have a successor, the number of organic farmers would decrease and the diversity of organic farmers’ farming styles as well. The importance of successors in conversion to organic is, however, in contrast with studies that found no correlation between age and early or late adopters of organic farming ,vertical farming racks or between age and farmers with a conservation identity.

The role of successors in adoption of alternative farming practices might, therefore, be different per context. Finally, in this research we used a predefined network for farmers, predefined characteristics for farmers and markets, including the distribution of markets and farming styles, and the instantiation of attitudes among farmers with a specific farming style. Different instantiations could have an effect on model results. For example, a distribution of age that better resembles reality , could affect more changes in farming styles in the model and, therefore, more organic farmers, or more quitters. Future empirical research on farmers’ characteristics including their network and/or sensitivity analysis on the instantiation of market parameters, attitude distribution, and pig farmer characteristics including their network can give more insight in the effects of different initial situations on diffusion of added-value markets. In addition, it would be interesting to gain more insight in social influence mechanisms among consumers that affect demand of organic meat, given a static price, through e.g. agent-based modelling. Currently it is challenging to find out how empirical and sociological knowledge can be brought to bear upon policy advice. In this research we used the social identity theory to gain better insight in the mechanisms behind social influence. As discussed above, this gives a good starting point for operationalizing social influence and in some parametrization scenarios similarities can be found in model outputs and trends in the pig farmer population. In others, contradictions still exist . Agentbased modelling serves as a good method to further explore how social identity theory affects decision-making and macro patterns, by identifying reference groups, status symbols per reference groups, and changes in reference groups. Specific model operationalisations should, however, still be explored further, such as the value for thresholds, and the effect of only positive social influence versus positive and negative social influence. If policy makers aim to promote alternative farming strategies, they have several policy instruments at their disposition.

There are legal instruments for labelling and certification. There are financial instruments, such as payments for conversion and continued organic production, investment grants, and biodiversity offsets to penalise intensive farming practices through taxes. Finally, there are communicative instruments that focus on changing social norms in society . Since supply is in reality is largely regulated by an organic slaughterhouse, and consumer prices are kept stable by supermarkets, the level of demand for organic meat cannot be addressed via consumer price, and the price elasticity of demand cannot be exploited. This is a constraint on diffusion of organic farming, since our results suggest that price elasticity of demand is an important mechanism to increase the trend in demand to stimulate diffusion of organic farming. Given the importance of the trend in demand for organic pork meat, two alternative financial policy instruments can be suggested. Both need further research to support the policy instrument that best fits organic farming diffusion. First, structural payments to organic pig farmers can decrease the cost price for organic pigs. A decrease in production costs should lead to a decrease in farm gate price, which in turn might lead to a decrease in consumer price. Given the high price elasticity of demand for organic pork meat , a lower consumer price for organic pork should lead to a high increase in demand for organic pork. Increase in demand then leads to increase in supply.If we assume that conventional pork consumers can be triggered by price changes to start consuming organic pork,biodiversity offsets to penalise conventional pork can equalize prices between conventional pork and organic pork. This does require more empirical research into cross-price elasticities of organic meat in comparison to conventional meat. Also, two communicative policy instruments can be interesting given the currently regulated market. First, communicative policy instruments can be developed that target consumer demand by, e.g., explaining the benefits of organic farming to consumers as to try and change social norms among consumers in favour of organic pork instead of conventional pork . The second direction for policy is to focus on new entrants, as this might contribute to diversity in farming styles in the farmer population and therewith diffusion of alternatives.

Communicative policy instruments can focus on improving the image of farming within society for the younger generation. Financial policy instruments can focus on easing the entrance of young farmers into the farmer population. Both are interesting directions to further explore. The European Green Deal includes an action plan aimed at promoting the efficient use of resources by moving to a clean and circular economy and aimed at restoring biodiversity and reducing pollution. The European Commission has developed guidelines to support the achievement of these objectives by “investing in environmentally friendly technologies” and by “supporting industry in innovation” . Precision farming has the potential to meet the challenges posed by the public ambition to produce more while consuming fewer resources . PF has been defined as “a concept of agricultural management based on observation, measurement and response to inter- and intra-field variability in crops or livestock aspects” . Not only are European institutions supporting it as a fundamental practice for the development of agricultural sustainability in the future, but they are also focusing on the political need to act to improve farming practices, for which it is necessary to “boost investments and uptake of new technologies and digital-based opportunities such as precision agriculture” . Studies have addressed the crucial role that data management will play in making farmers’ work more efficient through the use of combined technologies , remote sensors, smart tractors and operational focuses . In addition to these strictly technical factors, socio-ethical and environmental variables linked to the application of technological innovation , in terms of good or negative impacts on agriculture in the future, should be considered . Today, new responsibilities have been assigned to agri-food systems along with the challenge posed to innovation processes to produce a desired scenario, especially in environmental and socio-ethical terms for the agriculture of the future . In this sense, the need to boost a more responsible adoption of innovation paths is emphasised within the field of responsible research and innovation . Only a few studies have examined the extent to which RRI principles have been applied to PF . In particular, the development of a framework of responsible innovation in agriculture requires the study of 4 dimensions : anticipation, inclusion, reflexivity, and responsiveness. Anticipation is related to the ability of research and innovation policy to anticipate an unfavourable scenario in terms of potential environmental and socioethical risks, while reflexivity has been defined as the promotion of new reflection processes around innovation processes by including all prospective actors to reduce negative impacts while enhancing positive outcomes.

Furthermore, the inclusion of stakeholders and bottom-up governance strategies have been proposed as principles for promoting engagement in innovation processes and innovation trust. The last principle, responsiveness, includes the ability to support a change of course within innovation processes once new knowledge, emerging challenges or needs have been discovered. The RRI literature has risen to prominence in relation to numerous technologies that have high potential but considerable uncertainty. However, applications to precision farming technologies are limited . Analysing the desirability of PF through the RRI lens may be very useful since scholars have highlight the notable difficulty in the transfer and adoption of TI. As a demonstration, many authors have described several barriers to adoption, from the costs incurred to the difficulty of use , which can be fully captured by the concept that Vecchio et al. defined as the “complexity” perceived by farmers. Starting from these considerations, the aim of this study is to understand farmers’ perspective on the theme. Specifically, “In my opinion, precision farming is…” is the relevant issue from which this research starts. To answer this question, it is necessary to emphasise that the optimisation of production processes involves many dimensions of farms, such as the technological, economic, institutional, and behavioural dimensions . These aspects can be interpreted as pieces of a conceptual puzzle, vertical rack system of which some have been widely investigated, while others have not. In the field of research on agricultural innovation, the aspects involved in the sphere of the self of the farmer, that is, “a set of behavioural aspects such as motivation, emotion, relationships, perception and cognition” , have been less explored. Only a few authors have explored this dimension, mainly addressing the theme of perception . These studies have described perception as a functional variable in the adoption process, defining the main links with other pieces of the puzzle. In the complex adoption framework, our aim is precisely to colour the picture of farmers’ sphere of self and, more specifically, farmers’ perceptions. This study, however, intends to attribute to perception a character of exceptionality due to its subjective nature as an element belonging to the cognitive sphere. For this reason, this work proposes to study the adoption process by proposing a theoretical framework in which perception is isolated and analysed on a different level than that of the other pieces of the puzzle. Such analysis enables us to capture the views of farmers by overcoming the inevitable bias caused by the design of surveys aimed at establishing functional links . To that end, our survey focuses exclusively on farmers’ perceived meaning of the term “precision farming”. Furthermore, we accept the relevance of subjectivity in filling the content of the sphere of the self. Going into the cognitive sphere in the context of the self means carrying out an in-depth investigation. As with any survey and methodology chosen, it involves having biases but at the same time being able to collect very detailed information that is difficult to capture with quantitative techniques and tools. Although the results obtained cannot be generalised, they represent important insights and enrich the knowledge of the subject under analysis. Consistent with these aims, we use the Q methodology , theorised by Stephenson in 1935 and defined by Brown as a “bridge”. According to Durning and Ellis et al. , the QM is useful for analysing the transition between positivism and post-positivism aimed at the study of subjectivity. It mixes qualitative approaches, which are necessary to capture the multiple facets of subjectivity that escape numerical reduction, with quantitative techniques that help to measure results.

The models of the use case ‘Added value weeding data’ are described in more details as an illustrative example. Table 4 provides an overview of the applied control models. Point of departure in all use cases is a particular farm crop or animal, i.e. potato, tomato, lettuce, cow or pig. These objects are nested in high-level objects, such as fields, greenhouses and stables. The plant use cases all also predict the expected output of the farming process, i.e. potatoes, tomatoes or lettuce to be harvested. Two use cases also virtualize equipment used, i.e. a weeding machine or truck. All use cases analysed combine multiple types of Digital Twins, starting with monitoring the actual state of objects and then predicting future states e.g. expected yields or animal health. Most of the use cases also include intelligence to advice interventions. The crop farming use cases also process these advices into prescriptive Digital Twins, e.g. by defining task maps. Most use cases focus on the usage phase of a lifecycle and do not include imaginary or recollection Digital Twins. Only the use case ‘Added value weeding data’ applies a recollection Digital Twin for optimisation of machine settings based on historical data about machine behaviour. None of the use cases have implemented yet autonomous Digital Twins. Table 5 lists the main technical components that are used to implement the layers of the Digital Twin technical architecture. It shows that in the Device Layer all use cases apply domain-specific sensors and three use cases also use specific actuators. Most technologies for technical communication are based on standardized protocols of both conventional technology such as wired networks and recent wireless IoT networks such as LoraNet. The use case ‘Happy cow’ has chosen to apply a custom-built network consisting of distributed access points that enable communication up to several kilometers. Also in the IoT Service layer a combination of technologies is used.

Process-based orchestration of services is not yet addressed. Only the first use case ‘Within-field management zoning’ includes some Modelling Services. In the Digital Twin Management Layer all use cases provide services that combine and store data from diverse data sources and represent harmonized virtual entities. These services also include intelligence for simulation or decision support dependent on the supported control functions . In the application layer,hydroponic bucket all use cases provide dedicated dashboards for the interaction with users and three of the use cases also integrate with existing farm management systems . Finally, all use cases comprise some generic technical functions for the service organisation, security and management. When growing organic vegetables, weeding is one of the most important and frequent activities to control both the quality of the field and its produce . In recent years, automated intra-row weeding machines have entered the market, enhancing the weeding process significantly. The most advanced weeding machines use machine vision applications to distinguish crops from weeds. These camera data can not only be used for automated control of the weeding task, but also as a valuable information source for farm management. This use case uses these location-specific camera data of a weeding machine as a main data source to provide actual insights into the number of lettuce heads growing on the field, the plants’ growth status, weed prevalence and best harvesting moment. As such it creates Digital Twins of a field, plants and weeds to monitor crop growth and to predict the crop weight and size of lettuce. The applied control model of the use case ‘Added value weeding data’ is shown in Fig. 10. The main farming processes are sourcing and planting young lettuce plants, producing lettuce in the field, harvesting lettuce which is ready for consumption and delivering it to the market. The main physical objects involved are planting machines and young plants, fields containing weeds and growing lettuce, weeding machines, harvesting machines and harvested lettuces. The Digital Twins of this use case are used for monitoring weed pressure and crop growth, controlling the weeds to be removed and predicting the optimal moment of harvesting. To do so, the sensor function uses processed camera images to calculate crop parameters such as size.

Furthermore, crop growth sensing adds weather data and field properties, including temperature, relative humidity, wind speed and direction, solar radiation and soil moisture . The data acquisition function also includes external weather data. These data are then transformed into Digital Twins. The virtualisation in this use focusses on the field, which implies that the main Digital Twin is a high-precision and actual heat map of a field. A field map comprises weed density and the number and size of crops , and the expected final weight and crop size of the lettuce . Planting seedlings are excluded. The Digital Twins of the individual lettuce crops and weeds are used by farmers during the weeding activity and afterwards the calculated parameters of every plant in the field are also available remotely. Furthermore, Digital Twins of the weeding machine is used to optimize machine settings afterwards . The discriminator function uses the Digital Twins of weeds and growing lettuces to monitor weed pressure and crop growth, i.e. crop size and crop distance. The decision maker function translates the weed pressure into a planning of the weeding activities.The user sets a target value for crop weight and then the optimal harvest moment is determined. Based on this information the optimal moment of harvesting is determined and the harvesting is planned. For lettuce, growers get paid by lettuce head in the right weight class. Finally, the effector function executes the planned weeding task. The weeds are automatically removed, controlled by the actuators in the weeding machine that apply machine instructions based on Digital Twin of the weeds. Because of the high-precision weed density maps, fields can be weeded partially, only where needed. Also the planned harvesting activities are executed by harvesting machines but they do not use customised machine instructions. The control cycle partly takes place on-site within the weeding machine. Camera data are directly processed into local Digital Twins that distinguish crops and weeds. These Digital Twins are then instantly translated to actuator instructions and the weeds are removed without human involvement. However, all other control activities are done remotely by farmers who interact with the Digital Twins via cloud-based systems.

The next section elaborates on how this is technically implemented. Digital Twins can be seen as a new phase in smart farming. Using Digital Twins as central means for farm management enables the decoupling of physical flows from its planning and control. As a consequence, farmers can manage operations remotely based on real-time digital information instead of having to rely on direct observation and manual tasks on-site. This allows them to act immediately in case of deviations and to simulate the effect of interventions based on real-life data. The main contribution of the paper is that it has proposed a conceptual framework for designing and implementing Digital Twins for smart farming. The framework builds on an analysis of literature and a clarification of the concept of Digital Twins, which is still developing. An important novelty of the framework is that it adds a typology of Digital Twins based on the life cycle phases of the objects being virtualised. Depending on the perspective, the emphasis is currently often on monitoring or predictive Digital Twins. However, Digital Twins can already be created in the design phase of a life cycle and support the creation of its physical, real-life sibling. During operational usage, Digital Twins can not only be used to monitor and simulate the effects of interventions, but also to remotely control an object by using actuators. Finally, Digital Twins are also very valuable after disposal of a physical object e.g. for traceability, compliance and learning. So far, these distinct Digital Twin types are not explicitly addressed in the literature, which results in conceptual confusion. This paper has contributed to avoid this by introducing a typology and by defining the distinct control capabilities of each type in a control model. The case studies show that there are already applications in the agricultural domain that are not framed as Digital Twins. This is not surprising, since Digital Twins are building upon existing technologies especially for precision farming, internet of things and simulation. However, especially more advanced applications, including e.g. predictive and prescriptive capabilities across the lifecycle, are still in an early stage of development. The designed framework was useful to explicitly describe and analyze how Digital Twins are used in practice. As such, it has provided a new perspective on the cases that originally focused on the innovative application of Internet of Things technologies to farming. It also showed the value of not yet applied Digital Twin types, which inspired the use cases about potential redesign scenarios.

For this reason, we expect that applying the Digital Twin concept, as described in our framework, can accelerate the development and adoption of Digital Twin solutions for smart farming. However, future research is needed to provide evidence for this hypothesis. Furthermore, the implementation model of our framework only deals with implementing the enabling information technology. We did not take into account organisational and behavioural issues, such as the impact on supply chain collaboration, data ownership and governance, stackable planters the potential emergence of disruptive business models based on Digital Twins, ethical considerations, and so forth. We would like to encourage researchers in these disciplines to also study Digital Twins, since these non-technical issues might be decisive for the success of Digital Twins. Our intended follow-up work is related to the further development of the framework. In particular, we plan to elaborate the conceptual framework into an information architecture framework, which will comprise a consistent set of architectural viewpoints for modelling Digital Twin-based software systems . This architectural framework will be the basis for developing Digital Twin applications that cover the entire life cycle. Many farming systems in Europe are struggling with substantial challenges resulting from fundamental changes in their economic, technological, demographic, ecological and social environment . The resilience of farming systems, i.e. their ability to cope with and respond to shocks and stresses, has therefore become a major concern . The Covid-19 pandemic and the measures for its containment – e.g. lockdowns, travel restrictions and border closures – were expected to add another shock to farming systems. Using 11 indepth case studies, this paper investigates the extent to which different farming systems across Europe were affected by the crisis, which resilience strategies they adopted, and which characteristics enabled or constrained their resilience abilities. This paper contributes to a fast-growing literature on impacts of the Covid-19 pandemic on different parts of agricultural and food systems, e.g. food value chains, marketing channels, trade patterns and food security . Impacts on different farming sectors, e.g. due to production and demand distortions, have also been discussed . Others have reflected on the resilience of food systems at large in the light of Covid-19 . However, a systematic assessment how characteristics of farming systems have enabled or constrained their responses to the Covid-19 crisis is missing. By using an elaborate framework to assess and compare the resilience of farming systems before and during the pandemic, this paper aims to enhance our understanding how different farming systems were exposed to the crisis, which resilience capacities were revealed and how resilience was enabled or constrained by the farming systems’ social and institutional environment. Section 2 explains the SURE-Farm framework to assess the resilience of farming systems and the special data collection on Covid-19. Results are presented in Section 3, followed by discussion and conclusions in Section 4. Following the social-ecological tradition of resilience thinking , we define the resilience of a farming system as its ability to ensure the provision of its desired functions in the face of often complex and accumulating economic, social, environmental and institutional shocks and stresses, through anticipating, coping and responsive capacities . The resilience of a farming system is affected by specific system characteristics, and by the enabling or constraining environment, in particular institutional arrangements and resource availability .

The physiological response of the common reed plants to As toxicity and its possible relationships with the growth parameters and nutrient composition of the plants were evaluated through a PCA. The analysis resulted in a total of eight components, of which the first four explained 75.5% of the variance. The first component  related positively the photosynthetic pigments levels in the leaves with plant yield,height, and P mass fraction,as well as with oxidative stress parameters in the roots  and in the aerial part of the plants. This component therefore shows that the growth and photosynthetic system of the common reed plants were not affected by the increasing presence of As in the nutrient solution, likely as a consequence of the formation of those compounds that acted as an efficient defense mechanism against oxidative stress. The second component  related the different As species determined in the plants positively with each other, and negatively with Cu and Zn in the aerial part of the plants and with P and K in the roots. This confirms that As accumulation did not affect the plant’s photosynthetic and oxidative status, but may have limited macro and micro-nutrients uptake in the roots and their transport to the aerial part of the plants. The third component  simply related the values of Cu, Fe, Mn, and Zn in the roots,while the fourth component  related the levels of Mn and Zn in the aerial part of the plants negatively with those of MDA and proline in the aerial part and with that of N in the roots. The latter component seems to relate the deficiency of certain nutrients, likely a consequence of As uptake and accumulation in the plants,with the formation of MDA and proline in the plants, which is also indicative of As toxicity. 

When the regression factors generated in the PCA were plotted,the highest As dose  was clearly differentiated from the rest of the treatments, linked to a high accumulation of As in the roots. The control replicates grouped together opposite those of treatment T10, associated with the levels of nutrients in the plants,hydroponic nft system while the rest of the treatments showed an elevated variability and no clear or consistent relationships with any of the parameters determined in the plants.Malachite Green,a triphenylmethane dye, is a multipleuse compound that is mainly used in textile industries and partly used in aquaculture in fungicides and ectoparasiticides. While the effects of MG on aquatic invertebrates and algae have been scarcely elucidated,Hidayah et al.  reported that MG in wastewater from either industry or aquaculture has been widely reported to be toxic to many kinds of fish with lethal effects at a concentration of less than 1 mg/L, with the dye and its derivatives being accumulated in aquaculture products such as fish, prawn and crab. It also possesses carcinogenic and genotoxic properties which pose a potential risk to humans and therefore, this dye has been banned in Europe, the USA and several countries. However, MG is still being used in some parts of the world because it is highly effective and easily available at low cost. It is also used domestically as a treatment for diseases of tropical fish and can be readily obtained by the public ; hence, concern about its illegal use exists. In Asian countries such as Bangladesh, MG has been reported to be used for the eradication of external parasites and fungal diseases in fish farming. However, removal of MG from aquaculture wastewater has received little or no attention compared to other pollutants. Consequently, contamination of MG in aquaculture waste could be expected with harmful consequences to the surrounding environment. Effluents from aquaculture usually contain high amounts of nutrients such as nitrogen ,phosphorus and organic compounds that either potentially cause algal bloom in receiving water  or, if high enough, can support vegetable production. 

To reduce water pollution problems, fishery industries in many countries including Thailand have been forced to treat their effluent in proper ways such as by the rational use of water and by the recovery of substances from wastewater. Hence, effluents from aquaculture have been used for garden applications or the production of hydroponic plants as a secondary treatment in the waste management procedure. In some management practices, such as the study by Somboonchai and Chaibu,four vegetables were grown in a hydroponic system integrated with catfish culture. However, the effluents from aquaculture such as shrimp farming contain not only nutrients but also other chemical substances such as antibiotics, herbicides and fungicides that potentially impact on the environment. A review by Carvalho et al.  indicated that pharmaceutical products, including antibiotics, hormones, analgesics and anti-inflammatory drugs, chemical compounds used for disinfection and cleaning, and endocrinedisrupting compounds can be assimilated by the plants. Therefore, while the potential for biomass production and nutrients recovery from wastewater are primary concerns in wastewater management systems,bioaccumulation of toxic substances is another aspect of concern. Additionally, increasing water scarcity in either dry regions of the world or in developing countries makes the reuse of wastewater in agriculture more important. Nevertheless, it is of interest to identify whether or not a practice is productive and safe for both the environment and human health. Several plant species can tolerate toxic substances by accumulating them in non-toxic forms or transforming them to either nontoxic or less toxic products. Most studies showed that textile dyes can be either adsorbed and accumulated or transformed to less or non-toxic substances by detoxifying enzymes, predominantly peroxidase, in plant cells. The dye MG was found to be transformed to 4-dimethylamino-cyclohexa2,4 dienone in Blumea malcolmii Hook. using enzyme laccase and the products had less toxicity toward Phaseolous mungo and Triticum aestivum when tested. Rai et al.  found that biodegradation of MG by Aloe barbadensis resulted in nontoxic metabolites, suggesting the possibility of using treated, dye wastewater for irrigation. Torbati  reported that activity of antioxidative enzymes, namely SOD, POD and CAT, in Spirodela polyrhiza L. was increased with increased MG in the bathing medium.

The activity of these enzymes allowed the species to tolerate MG at concentrations of 10 mg/L and 20 mg/L. However, knowledge on the degradation of synthetic dyes by vegetable plants is scarce since phytotransformation has been studied mainly in non-edible plants. Nowadays, the trend toward eco-friendly and sustainable production of any kind of product strongly influences consumers. The current study investigated the application of wastewater containing MG from aquaculture for the production of pak choy,a vegetable that is produced commercially in many Asian countries. It was hypothesized that being a member of the genus Brassica whose species usually have high antioxidant enzyme activity upon exposure to toxic substances,B. chinensis may have ability to degrade MG dye and, hence, tolerate the dye at the low concentration used in aquaculture. If so, the reuse of water contaminated with MG could be applied. However, some Brassica species such as cabbage  and Wisconsin fast plants  could take up and accumulate some toxic substances in their tissue, especially in the roots. Therefore, on the other hand, the MG dye in water may be accumulated in plant tissue and inhibit growth of the plant. Thus, the aims of this study were: 1) to study the effects of MG on the growth of B. chinensis and 2) to evaluate the accumulation of toxic substances in the edible parts of B. chinensis. The findings from this study will be useful for consideration in a wastewater management strategy, particularly for the reuse of aquaculture wastewater in crop irrigation.When the seedlings were age 7 d, the nutrient mixed solution was applied replacing water, and the seedlings were allowed to grow under ambient conditions to age 14 d before being transferred to the growth medium used in the growth experiment. Fourteen-day-old seedlings with 3e5 leaves and an average height of 8 cm were selected for the growth experiment. The seedlings were grown in nutrient mixed solution for 1 wk to allow for acclimation to the hydroponic growth conditions. The nutrient mixed solution was prepared from tap water and 1 ml/L of commercial A and B nutrient solution for hydroponic planting. The pH and electrical conductivity  of the nutrient mixed solution were monitored and maintained at 6.0e6.5 and 1.5e2.5 ms/cm, respectively.After 1 wk acclimation, 48 seedlings were distributed to four levels of MG concentration treatments: 0 mg/L,1 mg/L, 2 mg/L and 4 mg/L. The basal part of each seedling was fitted in a small plastic basket to hold the plant in an upright position and the baskets were fixed on the lids of 5 L plastic tub containers. One container with 12 seedlings was used for each treatment. The chemical formula of the MG used was C23H25N2Cl. The concentration of each treatment was obtained by adding the appropriate volume to make up 500 mg/L of MG stock solution to the nutrient mixed solution which hereafter is called the growth solution.

The pH and EC of the growth solution were monitored and maintained as mentioned above and the growth solutions were renewed weekly. The experiment was maintained under ambient conditions with the air temperature 24e29 C,nft channel relative humidity 41e60% and natural sunlight. Decolorization of MG in the growth solution at day 7 was detected spectrophotometrically using an ultravioletevisible spectrophotometer. The solution from each treatment  was sampled and measured for absorbance at 400e800 nm compared with the absorbance of freshly prepared solution at the same concentration. After 4 wk of growing, growth parameters  were measured. Then, all plants were harvested and each plant was separated into root and shoot  parts, and the shoots were stored at 70 C in a freezer for further tissue analysis. The roots were abandoned since it is normally a non-used part of this vegetable and it was impossible to separate the plant roots from the supporting sponge. The weight of the shoot was measured after drying in a hot-air oven at 60 C for 48 h and the final dry mass was determined. The experiment was conducted between February and March.The effects of MG on plant species have been mostly tested using seed germination and the plant seedling stage, with germination and seedling development being generally inhibited. The current study found that MG contamination in water also caused negative effects to B. chinensis, particularly at concentrations greater than 1 mg/L. From the results, the negative effects of MG were strongly evidenced on root growth which was reduced by 50% upon exposure to MG of 2 mg/L and 4 mg/L compared to the control or 1 mg/L MG treatments. Similar effects on root growth were found in Arabidopsis thaliana grown on medium supplied with 4 mg/L of Crystal Violet and MG. The stunted roots may contribute to the overall reduction in plant growth since the uptake of water and nutrients could occur only via root transport under the hydroponic growth conditions used in this study. Nevertheless, the overall growth of B. chinensis in the current study indicated that the plant tolerates MG at a concentration of 1 mg/L. MG has been suggested to be toxic to plants as it could be strongly absorbed on the surface of cellulose  and taken up through the roots and accumulated in plant tissues. Saranya et al.  found that the chlorophyll contents in Hydrilla verticillata decreased with increasing Basic Violet 14 dye concentrations from 5 mg/L to 25 mg/ L, although the difference was not significant at 5 mg/L and 10 mg/L, and this result supported the inhibition of the dye on chlorophyll biosynthesis. In the current study, there was no evidence of chlorosis in the B. chinensis leaves at the concentrations applied. However, the effect of MG on chlorophyll biosynthesis in this plant species should be better explained by pigment analysis. Dye contamination in either the water or soil usually causes a reduction in the total content of macromolecules such as proteins and carbohydrates whilst it usually induces the activity of several enzymes used for dye degradation in exposed organisms. Triphenylmethane dyes such as Crystal Violet and Methyl Violet could cause lower protein synthesis which consequently inhibited cell growth in Bacillus subtilis. Jayanthy et al.  found that soil contaminated with dyes from dyeing industries caused decreasing protein, total free amino acid and carbohydrate contents in Vigna radiata, whilst in the same plant, there were increases in the proline, glutathione and methyl glyoxal contents in either leaf or root tissue which indicated a response to abiotic stress. Moreover, the activities of lignin peroxidase, veratryl alcohol oxidase, laccase, tyrosinase and DCIP reductase were induced in Aster amellus Linn. and Glandularia pulchella  Tronc. upon exposure to the dye Remazol Orange 3R.