The specific transformation pathways that farms take can be conceptualised in terms of resilience

Resilience refers to the capacity of social-ecological systems to fulfil their function in changing conditions, thus withstanding disturbances and being able to adapt and transform while delivering on their main goal . Although resilience is sometimes portrayed as stability, resilient systems can—and should be able to—transform. The strategies through which a social-ecological system may retain its resilience can be characterised in terms of persistence or robustness, adaptability, and transformability . Robustness refers to the capacity of the system “to withstand stresses and anticipated shocks” . Adaptability, in turn, entails “the capacity of actors in a system to influence resilience” by, for example, changing “the composition of inputs, production, marketing and risk management in response to shocks and stresses but without changing the structures and feedback mechanisms of the farming system” . Lastly, transformability is about “the capacity to create a fundamentally new system when ecological, economic, or social structures make the existing system untenable” . Such changes can imply a changing function of the farming system . A farm system may employ different resilience strategies over time. The food system and the embedded farm systems are in a flux of constant interaction: the dynamics on both levels condition each other. The employed resilience strategy depends on the transformative capacities of the farm and the farmer—what they can do with the resources they have. This makes resilience a question of agency and power. In a situation where the regime is strongly locked-in, farmers’ choice space becomes substantially limited .

The pressures are manifest in how farmers are acting mostly as price-takers and carry the responsibility for mitigating environmental impacts in the food system . However,flower pot not all farmers are similarly affected by transition processes, which calls for analyses of the transformation pathways accessible to farms. Agency and power are longstanding areas of research in social sciences. Agency can be seen as the actors’ capacity to act, and it constitutes power, intentionality, freedom of choice and reflexivity . Power, in turn, is understood here as “the capacity of actors to mobilise resources and institutions to achieve a goal” . When resilience is understood as the capacity of a system to achieve its goal, the notion of power in achieving that goal is central to the analysis of resilience. Resilience requires adaptive capacity, which refers to the potential of system agents to fulfil their goals, act independently, and exert their own agency . As such, the concept of adaptive capacity is practically identical to the concept of social power. Analyses of resilience and adaptive capacity at the level of farm systems require identifying the kinds of goals farmers hold regarding food production, the resources available, as well as the capacities to utilise them to achieve those goals . Thus, even though the concept of resilience has sometimes been used without being attentive to the societal context, questions of regime reproduction, or social power , it holds potential in analysing questions of agency, power, and social justice related to systemic transformations As systems may employ very different strategies to retain their resilience, it is presumed that system actors also employ different capacities in accordance with their resilience strategy. Avelino argues that transformative capacities are different from capacities that reproduce the existing structures, as in the case of persistent or adaptive versus transformative types of resilience.

According to Patterson et al. , “Transformative adaptation approaches take as a starting point that power relations condition the options available to marginal and vulnerable groups to shape their own desirable futures, thus requiring keen attention to issues of social difference, power, and knowledge.” Tribaldos and Kortetm¨ aki see capacity development as a criterion for a just transition in the sense of whether food system actors can respond to transition pressures. Thus, resilience capacities depend on what people can do and be with those resources and goods they possess or have access to . How farmers as system actors employ their capacities is a function of their internal goals and the external conditions defined by the food system . When the distributive effects of external conditions fall unequally upon the food system actors, restorative justice can reveal new perspectives on mitigating these effects. Restorative justice approach is traditionally understood as a non-adversarial response to harm and conflict that derives from violations of law, rules, ethics, or a general sense of moral obligation . The concept originates from criminal justice studies seeking to repair the damage and restore the dignity and well-being of all those involved in causing harm . However, restorative justice has increasingly been acknowledged in the field of sustainability, particularly from the perspective of energy transition, nature conservation, food transition and human rights . The common characterisations of restorative justice emphasise face-to-face dialogue between different parties configured as offenders or perpetrators of harm and the subjects-of-harm . The latter is often conceptualised as a “victim”, a condition under which agency and relationship with offenders are to be transformed. The process of restorative justice involves a reactive mechanism to address the damage already done. In other words, the process seeks to restore justice within the structures of the existing system. Accordingly, the individual is expected to undergo a transformation process while the surrounding system does not change.

Recent proactive approaches to restorative justice have emphasised more anticipatory elements of restorative justice. This means involving a range of actors and adopting a forward-looking approach that is both preventive and strategic . However, to be genuinely proactive and transformative, justice cannot be achieved by restoring the status quo ex ante . We further argue that the main challenge of restorative justice during systemic changes is that the transformation is not only about individuals but the system itself. Thus, individuals cannot be easily ‘restored’ with the logic of a system on the move. In systemic transitions, this would mean that those at risk of becoming ‘transition victims’ should also have the opportunity not to become ones. However, the application of the restorative approach to sustainability transition is not unproblematic, as the actors who fall victim to the transition processes have at the same time contributed to the problems that call for a transition in the first place. To what extent this contribution can be credited to the deliberate choices of the actors or just to them operating by the rules of the game remains debated. However, the current financial position of farmers suggests that the system itself is the most crucial factor in delimiting their choice space. The just food transition poses a fundamental challenge to restorative justice; the food system itself is enduring a major transformation which is also expected from the actors within the system. We argue that a genuinely transformative and proactive approach to restorative justice should aim at resilience and capacity building not only in terms of the existing system, but also in terms of the systemic transformation. We now move on to examine farmers’ transformative capacities and then discuss our findings from the perspective of restorative justice. The research area in Eastern Finland comprises three provinces: North and South Savo and North Karelia . The area is characterised by a sparse settlement structure and rather unfavourable socio-economic development patterns. The area adds up to 18% of the total area in Finland and 10% of the total population, with 557,000 inhabitants.

On average, the farms in Eastern Finland are smaller than the national average, and the fields tend to be fragmented into small plots. The share of utilised agricultural area in Eastern Finland is 5% of the total area in comparison with the Finnish average of 7.4% . The climatic conditions and soil properties are particularly suitable for grass production, and consequently, the role of cattle production is pronounced with 33% of all farms in Eastern Finland being cattle farms in comparison with the Finnish average of 20% . A significant share of the yields produced on crop farms are used for feed on cattle farms in the area . Regarding farm sales,berry pots in Eastern Finland 68% comprises animal products in comparison with the 58% average of mainland Finland . This study is based on survey data collected during the mid-term evaluation of the 2014–2020 Rural Development Program of Eastern Finland . The programme addresses a wide range of social, economic, and environmental issues of farms and rural areas by channelling the funds of the second pillar of the EU’s Common Agricultural Policy for farmers, rural firms, and non-profit organisations. A survey request was sent to all farmers in Eastern Finland who had received agricultural support from the programme and who had registered an email address in the IACS farm register . All active farmers in Eastern Finland with at least 5 hectares of arable land are entitled to LFA support, and in Finland, the support encompasses nearly all agricultural land . As a result, 577 responses were retrieved, with a response rate of 9% despite several requests to fill out the questionnaire. The low response rate was partly due to unfavourable timing of the survey at the beginning of spring but is in line with many recent farmer surveys conducted in Finland. The survey addressed issues related to the farm and its production activities, the farmer and the farming family, farming as a livelihood, environmental aspects related to farm management, and the main types of subsidies received and their perceived effectiveness. The basic characteristics of the surveyed farms are presented in Appendix 1 in comparison with all farms in Eastern Finland and all farms in mainland Finland. The survey respondents farmed slightly larger farms than farmers in the area on average but were broadly representative of farmers in the area.

Most of the survey respondents were cattle farmers , followed by other crops and cereal production . Garden crops, especially strawberry and currant, are typical crops in eastern Finland and had a share of 9% in the dataset. We operationalised the concept of resilience according to the three dimensions of resilience: persistence, adaptability, and transformability. In addition, we also identified a non-resilient group. The operationalisation strategy was based on three variables: 1) the future strategic orientation stated by the farmer , 2) an additional open question related to the farmer’s strategic orientation asking the respondent to specify his or her plans, and 3) freely expressed goals for farming . Out of the 577 responses, 575 were analysable in terms of resilience; thus, the final dataset consisted of 575 responses. Coding farm resilience was an iterative process between the three variables. Table 1 presents the coding principles for each resilience group. In short, a farm was coded as persistent when the farmer aimed at business-as-usual and did not indicate development intentions. Those farms that aimed at developing the farm within the existing operations were coded as adaptable. Transformable farms indicated a deliberate search for a new direction for the farm business by diversifying the farm operations or doing something new in comparison with the existing operations. Non-resilient farms aimed to quit farming by retirement or moving into another business; they did not have successors and their intention was to lease or afforest the fields. The resulting four farm groups with diverging resilience orientations were profiled in terms of variables concerning the farm and its production activities , the farmer and the farming family , farming as a livelihood , environmental aspects related to farm management , and the main types of subsidies received and their perceived effectiveness , adoption of agri-environmental contracts, investment support, organic farming, extension support. These variables reflect the availability of resources, as well as how farmers make use of them and how they relate to environmental management at the farm level, reflecting the mobilisation of environmental values and motivations. A complete list of the variables included in the analysis is given in Appendix 2. To determine whether the differences between the resilience groups were statistically significant, ANOVA tests were performed for continuous variables for the comparison of means, and contingency tests were performed for categorical and dummy variables for comparison of the distributions.

Reproduction control is another important tool for flock management in dairy sheep

With average costs of roughly 1.50€ per each tag, it is the cheapest method among the three. However, it suffers from one disadvantage which could lead to several problems. Its application to the ear lobe of the sheep increases its possibility to be lost due to entanglement in bushes, trees fences, etc. Another problem has to do with the ease of removal of the tag, a practice used in various fraudulent activities regarding animal identifications and could be avoided using irremovable animal tagging systems. In case of tag losses, new tags are to be applied, which not only causes additional administrative work but also impacts the welfare of the sheep which have to undergo another piercing of the ear.In this case, the EID is enclosed in a ceramic bolus, which is then inserted into the sheep’s rumen using a designated tool . Although having a slightly higher cost of about 4–5€, its main advantage is its permanence and very low malfunction and loss rate. Boluses have widespread use and are currently applied routinely in many commercial farms. It is however a more complicated EID to insert, with sheep needing to reach a certain age in order to safely receive the bolus. Size reduction and proper insertion by trained personnel mitigate these problems, with the bolus total size and length being a key factor. As shown by Hentz et al. , smaller boluses could be inserted safely and efficiently to smaller ewes while retaining the internal positioning and reliability.Widely used in house pets and horses for animal identification, its use in livestock although permitted is very limited . The main reason for its limited use is the difficulty to remove the EID in the abattoir,and the tendency of early models to migrate from the original region of injection.Different studies however show limited migration patterns of modern glass and silicone enclosed injectable EIDs during their use in field conditions.A particular advantage of injectable EID is the possibility of it being used not only as passive information storage but also as a sensor for physiological parameters.

The use of temperature detecting injectable passive RFID/ EIDs is widespread in the management of smaller laboratory animals and was tested on bigger farm animals under different conditions . Its use in sheep has been shown to provide highly correlating data to that of core temperature measured via rectal thermometry. This concept is, however, stackable flower pots still in the experimental stage and its future applications are uncertain.Sensors applied on the individual animal are one of the key principles of PLF with tools such as pedometers and rumination tags are well known to dairy cattle farmers. They provide information on animal’s physiological conditions whether in real time or via data loggers downloading in key passages . These sensors collect data from the animal and translate it into physiologic status such as ovulation or lameness relevant to farm management . In case of extensive sheep farming, wearable sensors have been experimented in small-scale-controlled conditions as well as experimental farms . The main objectives of these sensors are to evaluate grazing and resting behaviours, which provide information regarding grazing patterns and feed intake as well as animal position and movement of the flock . Currently, two main types of technology are being tried in this field: accelerometers, especially the tri-axial type, and GPS systems. The third use of active sensors is in the case of social networks and behaviour such as heat and mating identification. Being a seasonal breeding species, a big focus in Mediterranean production is dedicated to out of the season mating in order to maintain constant milk production in contrast to the sheep’s natural cycle . Currently, a common practice is the use of a harness on the flock rams with colour for visual identification of covered ewes; however, the use of electronic activity logger is being tested .A system that measures movement in terms of the direction and speed of the sensor is attached to the foot, neck or head of the sheep. Evaluated by the software first, data are provided to the producer to assist in decision making .

The most useful data come from three axial accelerometers which record movement in a three-dimensional pattern. Field trials confirm the ability of such accelerometers to register movement patterns linked to behaviours such as resting, grazing, moving and running/playing or lameness . Even though accelerometers could be considered technologically matured, data interpretation and validation is still a subject for field research . Meanwhile, the collection and management of the data as well as energy supply to systems in the field present a big challenge for a widespread application. In recent years, the amount of research put into this system is growing increasingly especially in attempt to take a research ready prototype into commercial production . Therefore, accelerometers could represent in the near future a viable product.Especially when paired with geographic information system , it provides information on animal movement and disposition in certain geographical areas. Such a system could help evaluate the movement of sheep in a vast grazing area, between water sources, low and high land and in response to the presence of predators or wild herbivores . In the work of de Virgilio et al. , combined use of accelerometers and GPS/GIS was proposed as a PLF option for sustainable range land management. Such systems, however, are not yet operational in commercial farming due to relative high cost of each sensor and the need for high energy supply . Also, information gathered by the systems still needs interpretation and given the right value in a decision-making process.In a recent study by Mozo et al. , tri-axial was used accelerometer with specific software to detect rams’ mating activity providing a possible tool to measure service capacity of rams. A more mature system is the electronic Alpha-Detector which includes a harness for the ram with an active reader and transmitter which detects the ewes’ EID and transmits the data to a centralized computer. The transmitted data could be interpreted for frequency of mating, true and false coverings and the number of ewes covered. This system has currently passed the research phase and is being tried in field conditions for commercial production .

Other technologies include a concept produced by Laca regarding extensive management of animals which incorporates GPS, satellite communication of data from ‘mother collars’, short distance communication between the animals’ collars and feed management based on the elaborated data. The system is very complex and requires both costly technologies and knowledge of the herd dynamics for the identification of key individuals in their respective groups . The feasibility of such system is becoming widespread in Mediterranean dairy sheep farming due to cost and complexity, but may be relevant for other types of extensive farming that use larger grazing areas , or less contact with the animal . Other sensors include microphone and sound analysis of chewing sheep and monitoring urination in sheep and cattle in order to determine liquid and nitrogen emissions. However, the systems were only described as an experimental process and not yet ready for field implementation.Stationary sensors are another key element in the PLF concept, with different types of sensors such as temperature sensors, cameras, weights and automatic feeders are placed in key locations of a barn . These sensors collect data and usually communicate with the animals’ EIDs, providing real-time data for each single animal to feedback systems . In extensive sheep farming, there are several stationary tools such as AD, weighting crates or a walk over weight system. Although the systems are extensively tested and reached advanced stages of development, they are not yet accepted by dairy sheep farmers for widespread commercial use .An AD, in simple terms, is an automat system centred around a selective gate with the ability to distinguish and direct the passage of animals. Most of the AD systems are based on the recognition of animals’ EIDs as the selective criteria. In extensive sheep farm, ADs and EIDs could be used together not only for data collection and feeding control but also as a tool to reduce manual labour for the flock . Animal selection is one of the most labour intensive activities on the farm, especially in events such as sheering, parasite treatments and selection for sale.

Automatic drafters could also be coupled with weighting systems in order to measure the condition of a single sheep, directing lower weight animals towards supplemented feeding areas accordingly .Originally developed for grazing cattle, both systems were consequently adopted and modified of sheep farming as well. The WOW was tried in field conditions where it proved its efficiency, consequently expanding its use to sheep management . The system includes a one-way passage leading to a key stimulant which the animals are forced to pass through. The weighting platform is placed in this corridor, and it communicates with the animals’ EIDs on each passage. Data regarding each single sheep are stored and could be matched against similar passages in a single day creating a more reliable result. When used by itself, the WOW system helps to reduce labour with fewer personal needed for animal sorting activity while pairing it with AD systems can allow better control on supplementation feeding . This combination has been proven to be efficient in several studies as presented by a recent review by Rutter and by Gonzalez-Garcia et al. , making it a viable instrument for farm management. The WC on the other hand is used by actively separating single animal by operator closing doors in a passage corridor. This way, each animal is weighted standing still and isolated from others. In the WC, the RFID identification could be done both by handheld transponders or by fixed reading antennas,flower pots for sale consequentially allowing the analysation of data in real time. Commercial models are already available on the market . The collected data could be used for various purposes such as ensuring lambs are ready for sale or anthelmintic treatments. The last use is of particular importance considering the growing awareness to the amount of anthelmintic resistant parasites in grazing sheep and the health implications derived from it . For this purpose, coupling the WC with a self-dosing fluid dispenser is a currently viable option with commercial products already on the market such as Te Pari fluid dispenser .

Virtual fencing is an innovative method for extensive animal management that replaces physical barriers with electronically placed boundaries. Animals are prevented from passage by a system of visible and/or audible cues combined with electric stimulus. Although VF is not able to provide a full sealing of an area, its flexibility and potential applications has attracted a growing amount of researches as well as stimulating commercial development with products such as BoviGuard, NoFence and eShepherd™. The main advantage of such a system is not the complete exclusion of animals from certain areas, but rather the possibility VF provides to guide and move the animals according to pasture availability . However, VF cannot completely replace all fences, as the hermetic exclusion of animal is impossible without physical barrier. Therefore, due to security reasons and property rights , the external fences of the pasture remain necessary. By using visible and audio cues prior to the electric stimulus VF systems are aiming to condition the animals to understand the limits of their area. Although there is a variability among the individual animals in understanding these limits, as a group the herd maintains its position . There are several factors however, which limit the adoption of VF systems on commercial farms. The first is its cost, although the cost of the system was estimated in 200 000 £ for 100 animals in UK, its difference is not as big in comparison with traditional fencing costs in the same country . However, VF cannot completely replace traditional fencing and a combined use will be always needed . Another weakness is the lack of technological infrastructure in sheep farms ; this includes network coverage and IT-related skills and understanding. Without this, farmers may find it difficult to trust hi-tech systems .

The layered architecture for designing the system enables self-independence between layers

SDSS benefited from the greater public availability of spatial data and the more flexible software, which enables its integration/- modelling into the geographic information system. In addition, an open-source SDSS project known as MicroLEIS DSS aids agriculture soil protection and land sustainability. It comprises valuable tools and techniques for decision-making in a wide range of agro-ecological schemes. This system builds on statistics, databases, neural networks, expert systems, Web technology, and GIS applications. The SDSS for agricultural land management, helps in decision making for the land management of food crops. It also aids in testing, validating and sensitivity checking of the decision models. The study revealed that SDSS is developed on Compromise Programming modules to produce spatial information integrated with fuzzy set and analytic hierarchy. SDSS utilises input information in operation, for instance, information from field experts and its applications. Whilst noticeable progress has been made in digital support systems, nonetheless, most of the proposed DSS have been put forward to handle aspects related to precision agriculture, irrigation management and optimal farming. Additionally, not much, if at all, have been proposed around facilitating support for farmers in terms of addressing their enquiries, questions and complaints, and optimising the whole process efficiently, besides providing insights to the beneficiaries from the vast amount of historic data, and recorded experience. The aim of this study, therefore, is to design and develop a system by considering the unique requirements of the farmers into accessing information whilst enhancing the overall system’s usability and acceptability. That is, the proposed DSS enables farmers to access information and experts’ advice; for example,hydroponic net pots information regarding the choice of seeds to sow, optimal harvesting times, knowing how to treat and combat plant diseases and pests, weather/calamity based forecasting and advisory etc.

The system is designed using a client–server architecture, where the client-side is responsible for all user interactions with the system. Clients interact with the server through web services. The Server applications are deployed on server machines along with a storage for managing data sets. Apart from these services, the Agro Support Analytics system also provides user registration and login functionality. A user can interact with the online Agro Support Analytics Central Server from the client machine through a web browser. The Agro Support Analytics Central Server handles input connections from clients as well as it hosts user registration and login services. In order to execute user requests the Agro Support Analytics Central Server is connected to more back-end services; i) Farmer Complaint service, ii) Historical Search service, iii) Analytics Apps. The overall working of the client/server system is illustrated in Fig. 2.Software applications of Agro Support Analytics have been designed on the configuration and plugin-based mechanism. This mechanism facilitates support for new workflow management systems and algorithms without altering the core of the system. Since the scope of the project is broad and complex; the overall project requirements can be divided into different applications with varying degrees of independence between the applications. Each application is further divided such that the application logic and business logic can be executed across servers. Moreover, the system under consideration requires faster network communications, high reliability, and excellent performance. In order to fulfil these design requirements, the n-tier architecture, or multi-layered software architecture is employed where each of the layers corresponds to a different level of abstraction. The N-tier or multi-layered approach is particularly suitable for developing web-scale and cloud-hosted applications very quickly and relatively risk-free. N-tier application architecture provides a model by which developers can create flexible and reusable applications. By segregating an application into tiers, developers acquire the option of maintaining, modifying, or adding a specific layer, instead of reworking the entire application.

In practice, the tiered architecture greatly simplifies the management of the software infrastructure. In this project, the layered architecture followed is ’closed’, meaning a request should go through all layers from top to bottom. Since architecture is broken up into multiple layers, the changes that need to be made should be more comfortable and less extensive than having to tackle the entire architecture.In a given layer, software components that belong to a similar level are organised horizontally, where the components may depend on the processing of each other, and this also makes relevant components to stays in a single compatible layer. This allows for a clean separation between types of components and also helps gather similar programming code together in one location. By isolating the layers, they become independent from one another. In the layered architecture, although the components from one layer can interact with the components of another layer, but they do not directly depend on other layer’s components. Traditional enterprise systems use RDBMS while the NoSQL system is widely adopted due to its excellent performance and high availability for large sets of distributed data. Thus if, for example, we want to change the database from SQL to NoSQL , this will cause a significant impact on the database layer, but that won’t impact any other layers. The adapted layered architectural pattern reduces the communication overhead caused by network traffic to provide faster network communications and efficient system performance. The component-based layered architecture also makes the testing process simple and convenient as individual components from each layer can be tested separately.This consists of a back-end database service comprising of various types of data sets, files, and the database management system that manages and provides access to the project data.

The datasets are made accessible to the Information and Analysis Services layer by hosting them on the Cloud. The second major functionality considered in AgroSupport Analytics is a Farmer Complaint Registration and Expert Response system. This system involves the development of interfaces for the online complain management, which can be remotely accessed to queries. These complaints can be reviewed by experts to provide feedback or suggestions using Expert web-forms. In order to store farmer complaints and associated experts’ responses, a new OnLine Farmer Complain and Expert Response dataset storage is established to contain richer data as compare to the available historical complaints data acquired from the Egyptian agricultural departments. Based on this data, extended analysis and predictions could be made possible that goes beyond the natural language based textual processing. Other datasets comprise User Profile and Login Info that includes the profile and login information of the users and user logs and activity history that contains the activities and logs of the Users. The layer also includes Agro Big Data Storage that contains the Historical Complain Dataset, the Online Farmer Complain and Expert Response Dataset. Search and Analytics Services in the Service layer interacts with this dataset in order to extract information from it.The Information and Analysis Services layer contains back-end software components and provides authentication, persistency, and information services. The authentication is a RESTful web service that operates on top of the User Data Info dataset in the private cloud and authenticates the users. Depending on the authentication result, user access type, and privileges, the user is given access to the modules in the application layer. The Complaint Management Services interfaces between the Online Complain Management application and the Online Farmer Complain and Expert Response dataset can provide functionalities such as crawl the datasets; make a model based on the structure of dataset; and store both data sets and outcomes, data dictionaries including possible parameters’ values, such that these are query-able by other tools and services, and store and index the image files associated with data sets.

The Search Service provides a mechanism to directly query datasets from the Agro Big Data Storage for querying, indexing, and searching based on Historical Search Engine as well as Farmer Complain and Expert Response Data. The Analytic and External Weather Projections services will act as information services and provide an interface between Analytic apps and the Agro Big Data Storage. Based on the Analytic apps information request, these services can query the Agro Big Data Storage dataset and then can apply data-mining, visualization, and machine learning algorithms on the data and then return the information to the Analytic apps.This layer contains user-friendly front-end interfaces designed for farmers and experts to remotely access the web components containing static as well as dynamic content. The front-end content is rendered by the web browser. These components include the User Sign In and Sign Up module, Farmer and Expert Dashboards, and Online Complain Management System. User Sign-In and Sign-Up components are available to authenticate the valid system users. After Sign In, Users can view Dashboards that contains their previous activity and up-coming notifications. In the Online Complain Management System, Farmer can submit their new complaint along with the textual, audio, and imagery data. The complaints are reviewed by the Experts, and they provide feedback or suggestions using Expert interface. These web forms are supported both in Arabic and English texts. This layer also includes Historical Search Engine and Analytics Apps. Using the Historical Search Engine component,blueberry grow pot users can query the Search Services, which in turn calls the Agro Big Data Storage to find the closest response from Historical Complain Datasets. The Analytics Apps can include analysis and predictions on the existing and/or external data sources to identify and explore patterns of ‘cause effect relationships’. The Querying Service is designed as a web service to be invoked over HTTPS to interact with the Agro Big Data storage, as shown in Fig. 5. This service-oriented approach provides the option to expose the server-side functionality to the client application. It enables a transparent and easy setup for providing desired functionality to users as well as external services within an authenticated session. The implementation of Querying Service starts with user verification that utilises the identity retrieval method provided by the Agro Support Analytics gateway. This feature not only secures the system by authenticating all the incoming requests but is also useful for maintaining logs of user activities. After user authentication, Querying Service initiates a query-building phase.

The implementation of the query-building involves i) parsing of parameters provided by the user, ii) selection of appropriate data sets.To interact with the query and complaint management component farmer needs to register with system if he is a new user or he can enter his login credentials to see the query and complaint management page. The system sends an automated email to the farmers email upon his registration. After registration/login, farmer can see a dashboard, where they can see list of all previous queries or complaints that are submitted. For each query or complaint, a status parameter is available with three possible values, i.e., ‘unresolved’, ’in-process’, or ‘resolved’. When a new query or complaint is submitted, its status is set as ‘unresolved’ by the system. This status can later be changed as ‘resolved’ by the agroexpert or by the farmer upon the resolution. Whenever the status is changed, the system sends an automated email to the farmer’s email regarding the change in the query or complaint status. In order to raise a new query or complaint, the farmer presses a ‘‘New Query or Complaint” button and a new form appears where the farmer enters the title of the query or complaint along with a detailed description in free text. Farmers can also relate their query or complaint with several filters available on the web-page. For example, farmers can add information regarding his area or region and can associate their query or complaint with one of the categories such as profitable crops for a region, irrigation, harvesting procedures and timings, management issues, pest issues, plant diseases, weather/calamity-based issues, etc., as shown in Fig. 4. Farmers also have the option to relate their query with a crop and attach images or audio files related to the issue they are facing. The additional information that the farmer provides will help the supervisor/admin later to assign them to the appropriate agro-expert. After the successful submission, the farmers’ dashboard appears with the status of the new query or complaint marked as ‘unresolved’. Farmers have the option to click on a query or complaint to view its details and responses made by agro-experts and he can make multiple top-ups on a query or complaint before it gets ‘resolved’.Supervisor can view a list of all farmers and the queries or complaints submitted by them. When a new farmer registers with the system, supervisor receives an automated email.

A critical dimension of the afforestation agenda is finding the space – land – to plant trees

Meanwhile, qualitative analysis of farmers revealed that farmers strategic approach to cattle purchasing of fitting the system meant that behavioural interventions were of limited consequence: the fact that they chose cattle with low bTB risks was coincidental. It is possible that our results reflect the way our participants were drawn primarily from the dairy sector rather than beef or calf-rearing sectors. Framing cattle purchasing in terms of short-term needs rather than establishing longer-term supply chains may also have elicited less frequent mentions of trust, reciprocity and ‘good farming’. These alternative scenario framings may have enhanced the significance of our ‘good farming rating’ but was nonetheless revealed in our qualitative analysis of our general discussions with farmers during the game. Our methodological approach therefore raises questions for how other research on behavioural insights within agricultural policy might be tested. In fact, a recent review of the agricultural behaviour change literature found relatively few studies of behavioural interventions, most of which relied on education rather than behavioural insights. Moreover, whilst some innovative methodologies were found , others relied on experimental methods that provide little insight into the differences between control and intervention groups . Alternatively, multiple interventions are applied to multiple contexts making delineating their effects methodologically challenging . Whilst calls have been made for greater methodological quality of behavioural intervention studies in agriculture ,fodder system there is a risk that reliance on experimental methods overlooks the many and varied contexts of agricultural activities such as cattle purchasing.

A key contribution of our research is therefore to respond to these concerns and provide complimentary methods to address these challenges. Secondly, whilst ‘good farming’ has been explored conceptually in relation to bio-security, this study responds to Burton and Paragahawewa’s challenge of developing good farming measures for a specific bio-security practice. Although such measures are not without their problems, in relation to cattle purchasing we have shown that good farming measures can play a role in shaping farmers’ cattle purchasing decisions, forming an important part of farmers’ purchasing ‘radar’ used to match cattle to their system. The process of matching purchases to farming systems observed in our study reflects what Burton et al. describe as an attempt to build a ‘cowshed culture’ – a ‘self-reinforcing culture in which animals, humans and the physical structure all contribute to the development of farm specific ways of doing and being’. Designing and reinforcing a system that promotes ‘positive interactions’ between the human and non-human constitutive elements is central to a farm’s success. The purchasing strategy of ‘fitting the system’ therefore reflects an attempt to maintain such positive interactions. Indeed, as Hidano et al. suggest, ‘livestock purchasing practices seem to be shaped in the process of establishing cowshed culture, rather than farmers choosing “best” cows for their farms after considering a whole range of animal characteristics’. In describing how farmers seek to ‘fit the system’ through their cattle purchases, we have also highlighted the trade-offs that farmers must make. The absence of the perfect animal means that fitting the system requires ‘skilled craftwork’ to identify the best animals to fit the system whilst also recognising the limits to this work . These skills are reflective of the kinds of judgments made about stock when purchasing them such as their likely productivity based on their conformation, appearance and behaviour.

However, estimations of good farming are also relevant here. On the one hand, good farming metrics may play a role in helping farmers to decide which stock to buy by providing reassurance that the vendor is not ‘dodgy’ but an ‘honest dealer’ . On the other hand, whilst farmers reacted positively and more enthusiastically to our good farmer rating than traditional metrics of disease control, it was also simplistic and unable to capture all the dimensions of good farming. This may explain why personal contacts and reliance on long-standing trusted trading relationships are preferred by many farmers. Nevertheless, further development and testing of other ways of expressing good farming for bio-security should take place. For example, a pictorial farm portrait may help convey good farming status better than a simple metric. Such an approach, whilst ostensibly less objective, may allow farmers to build their own assessments and be comfortable with their limitations because they reflect their own cultural values. Indeed, as recent bio-security research has suggested, recognising and living with the limits to bio-security boundaries is what makes them work . Finally, In showing how this fitting process works for cattle purchasing, we have also demonstrated how farmers’ decisions reflect a hierarchy of second-order strategies in which first-hand experience of the animals and vendor takes priority over representations of good farming in satisfaction ratings or disease information but which is more important than financial incentives and aversion to financial loss. However, it is also the case that these strategies and the relative importance of different information will vary between different segments of the farming population and according to different disease contexts. However, it may also be the case that the social context of disease management may also play an important role in determining the use of information available at the point of sale but which is not factored into narrowly defined approaches to behavioural ‘nudging’.

For Michie and West , this suggests that a range of behavioural interventions that may include both regulatory and persuasive techniques is required in order to be developed addressing different behavioural mechanisms is required . For others, the main problem with attempts to alter behaviour through the provision of information is that they fail to secure ‘norm internalisation’ , providing only short-term solutions. This is particularly the case when they relate to collective action to manage risks that affect everyone such as disease control . The answer to this problem may lie in moving away from ‘neuroliberal’ solutions that ‘infantalise’ people as unable to deal with complexity towards approaches that seek to engage them in co-producing their futures rather than by-passing their irrationality . As Drury et al. show, when people view an existential threat in terms of the way it affects a community, they mobilise and coordinate collective solutions and ensure the community as a whole benefit rather than just the most able. The implications of these critiques for cattle purchasing is that behavioural change interventions may be most effective when they are designed and produced by the communities affected by them . Indeed, our research revealed that farmers’ purchases were already oriented towards disease management priorities when they reflected the priorities within private forms of regulation that had been developed within and by the farming industry rather than priorities that had been imposed by external regulators. This suggests that rather than focus on changing individual behaviour, changes to the organisation of regulation in which the private sector creates its own systems of bTB control and incentivized through contractual agreements with farmers may prove a more effective strategy of managing the movement of cattle. Advocacy for tree planting and ‘woodland creation’ in response to climate change has reached fever pitch in the UK and beyond – in many ways becoming the raison d’ˆetre of contemporary forest policy. The Intergovernmental Panel on Climate Change published their special report on ‘Climate Change and Land’ in August 2019 , which stressed the importance of afforestation for its potential to deliver high impact on climate change mitigation. Echoing this at the national level, the UK Climate Change Committee ‘Net Zero’ report was published in May 2019, recommending planting 30,000 to 50,000 ha of trees annually to meet commitments made under the Paris Agreement.

These reports gained significant attention in national media highlighting the need for afforestation and emphasising the need for changing diets and moves away from livestock agriculture . Numerous other articles have appeared across national, regional, and local popular press related to tree planting for climate change mitigation or reporting contemporary ecological and forest sciences in this subject area . In one particularly high-profile instance, July 2019 saw several media outlets reporting the publication of ‘The global tree restoration potential’, a paper by a group of environmental scientists led by JeanFrancois Bastin, in the journal Science .1 National media headlines associated with this publication highlighted the ‘mind blowing potential’ of forest restoration to remove green-house gasses from the atmosphere . Related posts on social news websites became among the year’s most ‘upvoted’ posts within days . This narrative, drawing together a verifiable climate change mitigation technique with the widely popular act of tree planting, has proved extremely popular amongst political leaders. During the UK’s 2019 General Election, for example, political parties sought to outdo each other with manifesto commitments to ever larger tree planting promises. Tree planting targets themselves have had impactful media coverage , and form a significant element of governmental policy .It is widely felt that much of the proposed afforestation across the UK will need to be undertaken on land currently used for agricultural production. ‘Marginal’ upland areas typically used for extensive livestock production are often highlighted as key opportunity spaces. As a climate change mitigation strategy,fodder system for sale large-scale tree planting is often deemed to compete for land with agricultural production and is frequently considered to run counter to the cultural attachment of farmers and farming to the land . Land availability and the related socio-cultural context, attitudes, and goals of the farming community are therefore central constraints here. There has been much analysis in this arena with explanations of poor engagement with woodland creation and management amongst the farming sector centring on the roles of economics, knowledge, cultural norms and practices, governance design and advisory services . These constraints are reflected in the very low rates of afforestation in the UK in recent years . In western societies the media wields considerable power in disseminating ideas and defining what is considered normal, or ‘popular common sense’ in relation to specific issues. Mass media actors and society interact in complex dialogues, co-producing public understanding and setting political agendas, including in relation to sustainability and land management challenges .

Within this, diverse media outlets interact in different ways with their target audiences. Sectoral, local, and other membership-oriented media have a distinct role in reflecting, defining, and evolving or maintaining particular sets of understandings and values within relevant social groups . Whilst the media is not generally the immediate or direct motivation for farm-level ‘decision making’ , coverage of issues affecting the agricultural sector shapes farmer behaviour and decisions by representing issues in particular ways, expressing certain values, including or excluding topics, and outlining risks and opportunities for change . Thus, the farming media actively ‘frames’ agricultural practice by purposively including, emphasising, and promoting particular aspects of farming business and life, whilst omitting others. Given the context of an increasingly frantic drive for afforestation and the importance of attitudes towards trees amongst the farming community, in this paper we examine how tree planting, or ‘woodland creation’, is featured within and represented by the UK’s farming print media. Whilst digital media and sources of information are increasingly prominent within the agricultural sector, print media sources – especially dedicated ‘trade’ outlets – remain important sources and communication channels . Hence, the framing and communication of woodland planting and its relation to climate change mitigation within these outlets is highly likely to both reflect and shape farmer culture, preferences, and goals in relation to this issue. A number of agricultural and other land management debates have been examined through the ‘lens’ of print media analysis – including with a focus on sector-specific press. Rust et al. , for example, analysed the framing of sustainable agricultural practices in the UK farming press to understand if this influenced farmers to adopt these practices. This analysis found sustainable farming practices were most frequently framed from an economic or agronomic perspective which farmers identified as common drivers of adoption. However, the study also highlighted the limited trust placed in the farming press by some farmers, who believed that, due to the need for continued advertising revenues, reporting tended to favour agribusiness.

The need for labor for instance depends on the level of automatization in agriculture

This is why after assessing critical thresholds, participants should also be stimulated to think about adaptations to improve their system to desired sustainability and resilience levels . Be it by steering away or actual exceeding critical thresholds to arrive at higher sustainability levels. Paas et al. suggest a back-casting approach, but other solution-oriented methods such as participatory multi-criteria decision analysis may also be appropriate . In any case, starting with a threshold assessment before solution-oriented participatory methods may create path-dependency, resulting in adaptations that lead to a reconfirmation of the current system where a transformation might actually be more appropriate. This path-dependency is likely to be reinforced by only inviting participants from within the farming system. Farming system actors are for instance probably biased regarding depopulation and a loss of attractiveness of the rural area, as it is related to farm closure. Considering the possibility that the closure of individual farms could be good for the farming system as a whole might go beyond the mental models of some farming system actors. Participatory methods involving so-called “critical friends” that have no direct stake in the system might help to overcome this obstacle . Involving external actors is especially required in unsustainable systems that persist through the agency of only a subset of stakeholders. It should be noted that critical thresholds are never static as they depend on the context .Critical thresholds may change because of slowly changing variables , which is also acknowledged in this study by presenting interacting thresholds across levels and domains in multiple case studies. Different domains could be addressed by including a variety of social, dutch bucket hydroponic economic, institutional and environmental challenges, function indicators and resilience attributes.

Using the framework of Kinzig et al. forced in particular researchers in some case studies to reflect on critical thresholds in the social domain, while focus of participants was more on economic and environmental processes. The framework of Kinzig et al. can hence show where knowledge of stakeholders is limited. This is an asset as exposing the limits of local knowledge is often lacking in participatory settings . Explicitly adding the institutional domain and a level beyond the farming system to the framework of Kinzig et al. may further reveal the limits of knowledge and improve the understanding of farming system dynamics. To further stimulate co-production of knowledge, the figures with interacting thresholds could be fed back to farming system stakeholders in a follow-up workshop. In addition, farming system actors could be stimulated to think about representative indicators for resilience attributes. These representative indicators could add local meaning and thus improve stakeholders’ understanding and assessment of the resilience attributes and resilience mechanisms . Becoming aware about a threshold can help reducing the likelihood of exceeding one . Indeed, assessing critical thresholds may bring the awareness that is needed to move away from the conditions that have caused them. Participatory methods that are more specifically aimed at social processes could bring about awareness of system actors. However, interrelatedness with processes in other domains are consequently likely to be lost out of sight. Still, specific attention for social processes in the conducted workshops can improve the integrated nature of the assessments, for instance by pre-selecting at least one indicator related to a social function and a resilience attribute related to social conditions.

For some case studies in this study, this would imply a suggestion that new functions and system goals are needed. Although top-down, this could initiate the process of system actors picking up this signal as being valuable and the process of redirecting the system as a whole to an alternative state . The study presented in this paper is a resilience assessment that is partly objectively and partly subjectively defined: we worked with a set of function indicators and resilience attributes selected in a previous workshop by stakeholders based on lists prepared by researchers . Such an approach may not be feasible at EU scale, but has proven effective for postulating candidate indicators for monitoring frameworks such as the CMEF. More participatory workshops in a diverse range of EU farming systems are advised to find more of these indicators that can enrich those monitoring frameworks. It should be noted however, that assessments inclining towards a subjective definition and evaluation of resilience are poorly researched and that translation issues and cultural biases can limit these kind of assessments . Further elaboration and study of participatory methodologies is therefore necessary to improve its use for evaluating sustainability and resilience at farming system, national and EU level. Specifically the desired or acceptable degree of objectivity vs. subjectivity in assessments across different levels and domains should be discussed. Low-carbon societies and carbon neutrality have become key goals in combating climate change . Carbon neutrality is expected to both contribute to climate change mitigation and require adaptation in the agricultural sector. Developing the systems required by a low-carbon society is a process based on natural and agricultural sciences. For example, carbon neutrality needs changes in land use practices in farming. However, as it also involves political, social, and economic processes, the systemic change required in its implementation is extensive. The inclusion of farmers in the transition process and an understanding of their perspectives on the change are required, in part, to achieve carbon neutrality. Studies on farmers’ climate change perceptions have predominantly reported a majority of them being skeptical of both the anthropogenic nature of climate change , and its risks to their livelihoods . Consequently, it seems unlikely that farmers would be willing to proactively make considerable investments in carbon-neutral farming methods.

To improve the acceptability and adoptability of low-carbon policies and to better acknowledge their unwanted consequences, especially to vulnerable groups, the concept of a “just transition” has emerged and gained momentum. An example is the European Union’s Green Deal program . This concept, as the name suggests, focuses on the fairness of the transition towards low-carbon societies . The concept, which could be an important tool in improving low-carbon policies and policy-making processes, has expanded and become both more theoretically robust and academically interesting . However, it has been insufficiently utilized in the agricultural sector, although there is growing interest therein . Conversely, consideration of private companies’ perspectives, for both the agricultural and transitional processes, is also important. Private companies operate dairy chains, and dairy farms are an essential part of these chains. Dairy production currently faces many challenges, majorly in relation to discussions about its environmental impact. Demands for decreasing meat and milk production have increased , while the legitimacy and continuity of dairy farming; practices, livelihoods, and the entire sector have been disputed. In Finland, the combined agricultural emissions from the EU’s effort sharing sector and land-use are about 20% of the total carbon emissions . Much of the agricultural emissions come from the use of peatlands, which are strongly connected to dairy production . The level of the agricultural emissions has remained stable and there is a pressing need to find ways to reduce these emissions. Within this challenging situation, we scrutinize the transition towards carbon-neutral dairy farming in Finland. The aim of this study is to clarify how to shift towards carbon-neutral dairy farming in Finland, such that dairy farmers can see the systemic change as equitable. The study focuses on Valio’s carbon-neutral milk program. We acknowledge that the environmental measures promoted by the program are produced in this context. These measures are geared towards improving the practices and the profitability of the dairy sector. The program does not involve critical elements such as promoting the reduction of dairy consumption or limiting the number of livestock, although these would have beneficial climate impacts. This study does not aim to analyze the environmental impacts of the program but focuses on understanding farmers’ perspectives on the role of such private sustainability initiatives for the promotion of a just transition. We used a case study methodology to answer these research questions. First, we outline the theoretical framework of the study. Second, we describe our research data and the methods used. Third, we present the results of the study.

The results are divided into three sections according to the three main themes that arose in the interviews: 1) the profitability of farming, 2) concerns and blame in the context of dairy farming, and 3) use of agricultural peatlands. Finally, we discuss the results in terms of the two research questions and draw meaningful conclusions. The concept of a just transition has evolved in relation to sustainability transition studies and various interlinked conceptualizations, such as environmental, energy, dutch buckets system and food justice . In the environmental justice literature, it is common to consider a just transition in terms of a set of justice dimensions. The most commonly used dimensions include distributive, procedural, and recognitive justice . As compensation for injustice may be required, the dimension of restorative justice is also relevant. Distributive justice focuses on the distributive impact of a transition. Traditionally, at the core of sustainability discourse, there has been an interest in intergenerational equity: that is, a concern for the needs of future generations. However, distributional concerns need to account for intragenerational equity too , aiming for a balanced distribution of drawbacks and benefits among different actors in contemporary society . If an unjust distribution cannot be avoided, restorative justice can be used to compensate for the harm caused. For farmers, this could mean subsidies for changing farming practices or production lines. Procedural justice highlights the decision-making procedures used to reach and implement a sustainability transition in which every party should have an equal opportunity to participate. Finally, recognitive justice is related to procedural justice, but extends towards the recognition of different livelihoods and ways of knowing and being in society. In particular, this means the equal valuing of different cultures, with particular attention paid to vulnerable groups and elements of society, such as indigenous peoples . While farmers are not generally recognized as a group potentially at risk, owing to climate-related policies , their vulnerability in the food system has been acknowledged . As climate policies are shifting from a focus on energy to other key emission-producing areas, it is important to consider farmers and other workers in the land use sector.

Despite the recent interest in the concept of a just transition, empirical studies have largely focused on energy justice and the transition from coal in the context of coal mine closures . While farmers have not been studied previously in the context of a just transition, their perspectives on agri-environmental policies, climate change, and associated justice issues have been widely studied, providing important insights. The changes required in agricultural production also raise questions related to regional viability and livelihoods, which are at the core of current EU agricultural policies. Despite efforts to provide sufficient livelihoods from agricultural production and to support investments in and changes to production lines, farmers may perceive the support system as unjust. In particular, this relates to gaining a livelihood from food production, versus so-called quasi-farming, where fields are maintained without productive goals. Another distributive justice issue for farmers relates to profit distribution among food system actors, visible in the food sovereignty movement , and the emergence of diverse alternative food systems, which farmers may see as a way of obtaining equal payment for their work . The transition literature discusses restorative justice as a means of compensation for or alleviation of the distributive harms caused to particular groups, owing to transition or related policies . Restorative justice involves means, such as adjustment periods, education, and direct subsidies, to support structural changes. In the EU, agri-environmental subsidies follow the logic of compensation for the additional costs that implementation of environmental measures incurs. Undoubtedly, subsidies can also serve as a basic income. However, the changes required to reduce the climate impact of food production are likely to require more than mere adjustments to farming practices. Thus, the measures required for just compensation may also need to be wider in scope. Farming generally means more than just gaining a livelihood. It is a way of life, intertwined with one’s family, home, and local environments . These issues can be considered in the light of recognitive justice. For instance, similar to farmers, for mine workers and the mining community, the coal mine represents more than just a job.

Mountain farming faces several natural and technological limitations

A final element to consider in creating smart farming innovation processes that yield more effective configuration comes to light from actions in India. Over the last few years, a team of Berkeley University technologists, economists, and development practitioners has worked with the government in Andhra Pradesh to create ‘smart villages’ . Reflecting the vision that innovation processes can deliver effective results when they are open, as argued by Chesbrough in particular, the plan was to bring the team together to produce new sociotechnical arrangements in one village, Mori, that would empower villagers, improve their material situations, and yield insights about how to ‘scale up’ the interventions across the entire state. There is no evidence to suggest that people in Mori wanted their village to be ‘smart’ prior to the intervention, but from the outset the process was designed to tap the Mori crowd for insights in a form of co-design that identified specific problems that might be addressed by new technical fixes. One such problem involved the condition of textile weavers within value chains, which the ‘smart village’ initiative tried to address by creating a new ‘virtual village mall.’ Another problem concerned the structural relationship between farmers and the suppliers of agricultural inputs. To make the village ‘smart,’ the apparent solution was to create more direct connections between farmers and retailers. A partner on the project was the Indian agricultural e-commerce startup firm BigHaat. So long as farmers could access the Internet – as was facilitated by Google, one of the project partners – they could consider purchasing inputs directly from BigHaat and for a lower price than if they had to rely on various intermediaries. In this smart village, tapping the crowd informed and then guided a tech firm to create a ‘win-win’ solution: Mori farmers paid less for inputs, while BigHaat made new sales and,gutter berries crucially, created opportunities to learn from analysing data generated by the new flows of information when farmers tapped screens on their devices and communicated with BigHaat’s servers.

Writ large over the entire state – ‘scaled up’ – this new type of ‘smart’ engagement would conceivably lay the ground for further innovations based on tapping the crowd for insights. The smart village envisioned by this project would play a new role in expanded open innovation ecosystems designed to upgrade the technical sophistication of rural life and address societal challenges. Yet, the technical dimensions of all this action deserve critical scrutiny. Initiatives such as the smart village might empower some or indeed many villagers and they could improve their material situations. However, based on what we know about digital life in general, what seems much more likely is that these initiatives will generate significant scope for tech firms to create new assets and value from data flows ; assets and value, moreover, that they will not share with the users of their technologies. Whether framed as a matter of surveillance capitalism or data colonialism , an important dynamic of digital life concerns the maldistribution of opportunity to convert data curation into profits. The asymmetries of digital life mean firms such as BigHaat stand to gain the most from smart village projects. In this context, then, it is worth remembering some pertinent lessons from the green revolution. Consider that when India embraced green revolution practices in the 1960s, the government redirected scarce resources toward importing fertilizer needed to support the planting and growth of new green revolution wheat varieties . Part of the issue was a realization in India that, although the country had “doubled its output of machinery, chemicals, and power […] ‘you can’t eat steel’” . In the contemporary context – when investment in smart cities, villages, and farming is bound up with the notion that “data is the new cash crop” – the refrain ‘you can’t eat data’ might have some purchase, especially given India’s rush toward smart technologies despite malnutrition currently affecting around one-seventh of the population . The stark difference now, though, is that some of the lead actors in the production of smart life in India do eat data, albeit by virtue of their ability to convert data into profits.

In a place such as Mori, it is not so much that villagers can’t eat data but rather that the current rush toward using digital technologies is underpinned by approaches and economies that mean Mori’s villagers are unlikely to share in the harvest. The Mori smart village project yields a unique but striking type of misconfigured innovation. Given the growing number of similar digital initiatives rolling out in the shadow of high-level belief that digital technologies can “play an increasingly important role in achieving global food security and improving livelihoods especially in rural areas” , it is necessary to ask whether an emancipatory version of smart farming could do any better. What might be the intricacies of building innovation processes that reconfigure the sociotechnical relations of smart farming within the ‘planetary cognitive ecology’ to enable all food producers, not only those in the global north heartlands of smart farming, to eat data? In the context of significant inequalities in the ability of digital pioneers and laggards to take advantage of smart life, a minimum insistence of an emancipatory version of smart farming should be that adopting digital technologies works from the ground up to create incremental adjustments via information-intensive iterative processes that target systemic or structural change. In effect, the task should be to find models of emancipatory smart farming that use algorithmic affordances to pursue ‘productive resistance’ to dominant formations, such as the corporate food regime. The point here is, plainly, that new and potentially radical arrangements of digital platforms, devices, and software are waiting to be established. Thus, as outlined in the final column of Table 1, arrangements of devices, software, and practice that lead to something akin to emancipatory smart farming are at least conceivable. Departing from the mainstream model of smart farming, emancipatory smart farming arrangements will use technology to support agroecological and regenerative food production in a food sovereignty framework. Such arrangements would need to consist of hackable devices that users can repair. Open source software would be a requirement. If digital platforms are involved, for example to pool computational resources, they would be run as platform cooperatives. Users’ privacy would be built-in by default.

To the extent that data emerging from emancipatory smart farming arrangements will have value, it will be shared and held according to principles of data sovereignty. In all of these respects, therefore, emancipatory smart farming would depart significantly from mainstream practices. Further, striking differences pertain to innovation processes. An emancipatory smart farming arrangement would need to be constructed from the bottom-up in a participatory approach that empowers food producers to remain independent of ATPs. Ultimately, its aim would be to undermine, resist and overcome systemic challenges facing food producers. The point here is that, with novel innovation processes, it should be possible for even the most oppressed food producers to participate in the creation of emancipatory smart farming practices that engage digital technology in transformative ways. A key concept introduced by the European Commission’s “The future of food and farming” communication is that the next common agricultural policy post-2020 reform must foster a smart agricultural sector. As pointed out by the EC, “smart farming” or “smart agriculture” represents the application of modern information and communication technologies to agriculture, leading to what can be called a “Third Green Revolution” . ICTs include products and services that allow entrepreneurs to store, process, transmit, convert, duplicate, or receive electronic information. Among the ICTs for smart agriculture, farmers can adopt software and hardware solutions, such as professional applications and operating systems, mobile phones, remote sensors, and multimedia products . These technologies provide farmers with updated information, such as farms’ input and yields and agricultural markets, promoting an increase in the efficiency of the farm production process through evidence-based managerial decisions . Moreover, as reported by the FAO , ICTs can promote learning and therefore facilitate technology adoption among farmers. Despite the advantages provided by these technologies, in the last decade in the European Union ,strawberry gutter system only one out of four farmers adopted ICTs . Furthermore, despite Italy being the first-ranked European country in terms of agricultural value added and the second-ranked in terms of production value , grow strawberry in containers the last agricultural census showed that only 76,000 out of 1.6 million farms adopted organizational innovations such as ICTs . According to the European Innovation Scoreboard analysis , Italy has moderate innovation performance compared to the other EU member states.

In the agricultural sector, structural and cultural factors surely affect the innovation process, which is not uniform within Italy . Although the fostering of smart farming appears to be even more important for increasing the competitiveness of mountain farming, these mountainous areas show the highest aversion towards innovation .For instance, climatic conditions limit the length of the growing season and lead to the scarce accessibility of lands, the presence of slopes impedes the use of machinery , and poor mobile network coverage can hamper the use of ICTs . Such limitations imply some difficulties in the development of economies of scale and thus have a great impact in terms of increased costs and lower productivity compared to lowland agriculture. Despite these constraints, mountain farming’s persistence and prevention of land abandonment are essential for protecting landscapes and ecosystems, reducing erosion and natural hazards , supporting the local economy and preserving local traditions . Considering the crucial role of mountain farming in the provision of public goods to society, special support programmes for mountain farmers have been developed in the CAP since the early 2000s, and the current public policies increasingly support innovative practices in these areas, encouraging farmers to adopt ICTs to ensure agricultural sustainability . Despite all these efforts to promote ICT application in mountain farming, these technologies remain scarcely used in these areas .

Scholars and institutions have widely recognized the importance of fostering smart farming for improving mountain farming competitiveness ; nonetheless, to the best of our knowledge, no previous studies on ICT adoption have been developed focusing on a sample of mountain farmers. By means of the clustering analysis method, the present study examines how attitudes and the characteristics of farmers and farms influence the use of ICT devices . To the best of our knowledge, this is the first study clustering mountain dairy farmers based on their attitudes towards technologies. The results from this study are especially important considering the limited adoption and diffusion of ICTs among mountainous farmers. In fact, understanding the factors that affect the adoption of these technologies is fundamental for the development of tailored policies in support of different types of mountain farmers. Our results can also help service providers indicate future directions for the design of their products. The remainder of this article is structured as follows. Section 2 presents a literature review focused on farmers’ adoption of technologies and their attitudes towards innovation. Section 3 describes the methods and procedures that were implemented in the analysis, including the conceptual framework , the case study , sample and data collection and the data analysis . Section 4 describes the results, while Section 5 provides a related discussion. Finally, Section 6 provides a summary of the research and some conclusions. Most of the literature on the factors affecting farmers’ adoption of technologies and innovation in developed countries seems to be related to specific types of technologies . For instance, Wheeler focused his study on the adoption of organic farming and genetic engineering practices in Australia. Additionally, in the Australian context, Sneddon et al. investigated farmers’ adoption of new agricultural technologies in the wool sector. A number of studies have investigated the adoption of specific sustainable and pro-environmental agricultural innovations within the wine industry and more generally in land management . Tey and Brindal and Pierpaoli et al. investigated the factors influencing the adoption of precision agricultural technologies by summarizing the findings of past studies. In Italy, Cavallo et al. analysed the innovative attitudes of farmers towards the technological innovation of agricultural tractors.

Vietnam is among the most vulnerable countries in the world in regard to climate change

Our results demonstrate experimentally what has long been argued anecdotally, that farmers respond to price incentives . For organizations looking to provide contracts to farmers, this result is encouraging because it implies that they can provide strong incentives to farmers without undertaking the costs of providing training and input loans. By far the most binding constraint to expansion for ESOP is the need to raise sufficient capital to provide input loans to farmers at planting. Our results demonstrate that much of this expense may be unnecessary and ESOP could potentially expand the number of farmers it contracts with, and thus its throughput, by offering farmers a guaranteed price. With a price guarantee delivering secure market access, farmers can use the contract as collateral to rent in more land and obtain loans for inputs, improving outcomes for both parties and contributing to more rapid rural transformation.Since the plastics industry first flourished in the 1950s, global plastic production has steadily increased, reaching 368 million tons in 2019 . However, poor management of plastic waste means that it is frequently washed into the oceans, where it accumulates and disperses on a global scale, showing a great resilience . Studies estimate that the amount of plastic floating on the sea surface is between 93,000 and 236,000 tons, representing approximately 5000 to 50,000 billion fragments, 92% of which are micro-particles of plastic , also called “micro-plastics” . These micro-plastics can enter the marine environment by several pathways .

The majority of MP found in the oceans are secondary MP produced by the fragmentation of larger plastic debris under a combination of environmental factors . Primary MP, in contrast, are those directly released into the environment as micro-sized particles . Micro-plastics have been reported in all major oceans and seas including the Pacific , Atlantic and Indian Oceans , as well as the Southern Ocean , Arctic polar waters , Antarctica , and the Mediterranean and North Seas . They have been found everywhere, from populated coastal environments to the most remote areas . Their ubiquitous nature in all environmental matrices, from surface water,down through the water column to the sediments,grow table including in marine biota,as their small sizes make them easily taken up by a wide range of organisms . In French Polynesia , pearl-farming is the second most important economic activity, based on the trade of pearl and mother-of-pearl . It also contributes to the social development of the territory by being widespread across 23 remote islands and atoll lagoons. However, pearl-farming is associated with a specific source of plastic pollution. The inventory carried out by Andr´efou¨et et al. in the atoll lagoon of Ahe revealed thousands of tons of plastic pearl-farming gears . Rearing structures and equipment of these types are accumulating over time in pearl-farming lagoons. They may fragment into smaller particles, which then add to MP entering the lagoons from other anthropogenic pressures and from the South Pacific subtropical gyre . This situation is worsened by the semi-enclosed environments of some of these lagoons, which could favour MP accumulation.Pearl-farming could thus be causing a risk to itself through plastic pollution, with a potential impact of MP on the suspension filter-feeding pearl oyster Pinctada margaritifera. Indeed, exposure using polystyrene microbeads demonstrated a dose-dependent effect on the energy balance and dose-specific transcriptomic disruption to gene expression in P. margaritifera. However, these effects were only observed in experimental controlled conditions that do not properly represent the complexity of the environment.

Furthermore, concentrations tested were not ecologically relevant since no environmental surveys had been performed in pearl-farming lagoons. To date, only one study has demonstrated the presence of MP in French Polynesia waters, using a 50 µm-plankton net in front of a public beach in Moorea, where they reached 0.74 MP m–2 . There was, therefore, a strong need to evaluate and characterize MP pollution in pearl-farming lagoons. The aim of the present study was to evaluate MP contamination in pearl-farming atoll lagoons of French Polynesia with low population and tourism. We investigated MP concentration, composition and spatial distribution in surface water and the water column , as well as in the tissue of cultivated pearl oysters. Our study addressed two main aspects: the distributions and concentrations of MP in the compartments investigated; the identification of polymer types and relative abundance, in so far as the main characteristics of MP contamination could be related to those of local macroplastic pollution sources such as the widely distributed pearl-farming gears. The data produced should facilitate decision making for local government policies to assess and anticipate this emerging risk for pearl-farming sustainability in French Polynesia.Its agricultural sector is particularly susceptible to various damages caused by climate change . Close to 40 percent of the country’s total land area is agricultural land. The agriculture sector accounts for 24 percent of Vietnam’s GDP, 20 percent of total exports, and over 70 percent of total employment . Using integrated or multi-sector modeling, Arndt et al. estimated the economic cost of climate change in Vietnam and concluded that the annual GDP growth rate would decline by about 1%–2%. Even so, they found that the negative impacts on agriculture and roads would be modest by 2050. They further showed that adopting appropriate preemptive actions to climate change would bring positive results. Agriculture is an important pillar of the Vietnamese economy. Rice farming, which uses two-thirds of the country’s rural labor, produces 30 percent of the country’s total agricultural production value . Vietnam is also one of the largest rice exporters in the world .

More than half of Vietnam’s rice production and about 90 percent of the rice exports come from the Mekong River Delta.However, this low-lying area faces some of the worst impacts of climate change and is therefore seen to ‘‘severely compromise’’ the country’s future rice production . Recent studies have shown that ongoing climate change has had significant impacts on rice production and the livelihoods of farmers in the region. The most serious effect is caused by saltwater intrusion during the Winter– Spring crop season . In the 2015/16 W-S crop season, MRD farmers suffered great losses from saltwater intrusion as rice paddy production fell by 11.2 percent in comparison with the 2014/15 W-S crop season . The problem is likely to continue in the future. The sea level in 2050 is projected to be between 25 cm and 30 cm higher than the 2000 level, which will likely result in salinity intrusion of >4g/l up to 50–60 km from the mouths of the Mekong River affecting about 30,000 hectares of agricultural area . Local authorities have intervened to protect MRD farmers against drought and salinity intrusion. Assistance includes adjusting seasonal schedules, managing water resources, adjusting cultivation techniques, diversifying and changing crops, applying new varieties, and self-learning to protect crops and cut economic losses . Previous studies provided empirical evidence on the effectiveness of adaptation strategies as well as the factors that influence the rice farmers’ choice among various adaptation strategies . However, the benefits of such solutions were not properly controlled in the past comparative studies. Specifically, the following problems can be cited. First, studies that used a binary indicator to measure the cope-with climate-change solutions failed to quantify the costs or control the farmer’s response process. Second, the impact of each strategy on income and productivity could not be separated as the studies aggregated many coping solutions. Third, the use of annual outcome indicators such as costs, profits, and productivity could not identify the effect of a single-response strategy in each crop season since the seasonal weather factor is not controlled.

To avoid these problems, we investigate the role of an adjusted cropping calendar in the rice production of MRD farmers facing saltwater intrusion. We focused on the relationship between early planting and the production and welfare of MRD rice farmers during the 2019/20 W-S crop season. This strategy is based on the following considerations. First, Nguyen and Ho , Nguyen et al. , and Nguyen and Nguyen found that farmers’ adaptation strategies against climate change significantly affect their farm income. Because farmers are highly resource conscious when making climate adaptation decisions, a comparative study is required to gauge the change in farmers’ welfare under different adaptation strategies. Second, Lu et al. argued that the timing of sowing needs to be predicted appropriately to avoid risks and achieve maximum yields. In 2018, the Ministry of Agriculture and Rural Development of Vietnam instructed the provinces in the MRD coastal areas to apply Climate-Smart Maps and Adaptation Plans 1 to adjust the rice planting calendar during the 2019/20 W-S crop season to minimize saltwater intrusion brought by the 2019 El Niño . In 2019, ebb flow table experts predicted that saltwater intrusion in the MRD would start earlier and that the salinity level would be higher than those in the 2015/16 dry season. The Department of Crop Production issued Official Document No. 1252/TT-VPNN directing the MRD to adjust its planting calendar in the 2019/20 W-S crop season. Coastal areas of the MRD, including Long An, Kien Giang, and Soc Trang provinces, were advised to plant rice from early October to early November in 2019. This created an opportunity for a natural experiment for the current study. To the best of our knowledge, this is the first study to examine the impacts of the adjusted cropping calendar on the welfare of rice farming households in the 2019/20 W-S crop season.

To determine the effect of early planting in response to saltwater intrusion, a total of 1176 rice farmers in three MRD provinces were randomly selected as research participants. Then propensity score matching was applied to match 412 early-planter farmers with 764 non early planters ,comparing the rice farming income and rice yield of the two groups. We found that early planting increased rice farming income by VND 8.62–8.77 million per hectare and rice production by 2.51–2.59 tons per hectare during the 2019/20 W-S crop season. Our findings suggest that during salinity years, advancing the rice cropping calendar to early planting can increase the production and income of rice farmers. This confirms the significant benefits of crop calendar adjustment in areas exposed to the risk of saltwater intrusion. The result also corroborates the robustness of a ‘‘planned’’ response to climate change risks in agricultural production . It is also consistent with the framework for agricultural climate change adaptation advocated by Ozor et al. and the process model of private proactive climate change adaptation presented by Grothmann and Patt . The paper proceeds as follows. Section 2 presents an overview of the study site and rice cropping practice in the MRD. Section 3 describes the sample and the methodology. Section 4 reports and discusses the results. Section 5 concludes the paper. MRD farmers practice either two or three rice cropping systems. Normally, the Winter–Spring crop season is from November to February, the Summer–Autumn rice season is from April to July, and the Autumn–Winter rice season is from August to November. In normal years with no drought or high salinity, MRD rice farmers start planting in middle to late December for the W-S crop season. Rice farmers can move the planting calendar forward, particularly for the W-S season, whenever unfavorable environmental conditions such as drought and high salinity necessitate it Nguyen . For early planting, the DCP calendar recommends planting within the month of October during the W-S season, which is the season most affected by drought and salinity. Nguyen found that rice farmers in Long An, Kien Giang, and Soc Trang, especially those practicing the two-cropping system, define ‘‘early planting’’ as starting the planting period by mid-November. Considering this, we define early planting for the 2019/20W-Scrop season as planting rice by 15 November 2019 at the latest provided that this is the last cropping of the 2019/20 crop year. If the farmer planted before 15 November 2019 but had another cropping extending from late November to February 2020, he or she was considered a non-early planter for the 2019/20 W-S crop season.The target sample size of the treatment group was 384.

Similar constraints should be considered regarding available data on natural and semi-natural habitats

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

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

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

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

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

Hierarchical clustering was performed on 12 variables and this allowed four clusters to be retained

Like other countries in Sub-Saharan Africa where guinea fowl originated from, Benin, which is a country with a population of around 10 million people , is also involved in indigenous guinea fowl production. Mishra et al. ; Sayila and Traore et al. in their various studies found that guinea fowls are generally more resistant than chicken and turkey to most of the common poultry viral diseases such as Newcastle disease, Fowl pox and Gumboro. Guinea fowls are also well adapted to traditional breeding production systems and as such occupy an important position among rural farming households . Guinea fowl contributes immensely to rural households as it serves as a source of animal protein , income generation from the sale of eggs and birds, and thus improving the food security condition and consequently poverty reduction among rural households . Guinea fowl meat and eggs are an excellent choice, both gastronomically and dietetically, with a high protein and low fat content . Apart from income generation and nutritional  benefits, guinea fowl perform social and cultural roles in many African societies . Despite the socio-economic importance of guinea fowl in this region, it is faced with some challenges which hamper its optimum production among rural folks. Some of the challenges as documented by Traore et al. include a high rate of mortality particularly during the rainy season. Furthermore, earlier studies examined constraints to optimal poultry farming among households in the Sudanian and Sudano-Guinean areas of Benin. However, in the Guinean zone and selected districts in Sudanian and Sudano-Guinean zones, constraints on optimal guinea fowl production have received little attention from researchers. Also, dutch bucket hydroponic as guinea fowl farming is gaining prominence across all the three climatic zones in Benin and the scarcity of documented information about their socioeconomic correlates and characteristics across the country limit actions to improve and develop its production.

The aim of this study, was to gather data on indigenous guinea fowl farming practices in Benin and generate information that could enhance intervention strategies to combat the constraints and consequently improve the productivity of guinea fowl in Benin.The study was carried out during the second half of 2019. A cross sectional survey was carried out in the district outlined in Figure 1. Primary data was obtained through the administration of a questionnaire to each of the 165 local guinea fowl farmers sampled who reared at least 10 birds on their farm. Information on socio-economic characteristics of farmers, flock size, breeding and herd management technique as well as challenges against optimal production were elicited. Interview guide in local language was furthered employed among farmers as the majority were illiterates. This made it easy to validate the responses obtained from the respondents. In term of reproductive parameters, the hatch rate was computed by asking farmers the number of eggs laid by the birds per laying season and the number of eggs hatched. The mortality rate at one week and three months after hatching were also obtained as a ratio of the total number of dead birds to the total numbers of birds available atone week and three months respectively.The data collected was entered into Excel 2013, before being imported into the R software for statistical analysis. Multiple Correspondence Analysis was used to obtain a representation of farms in the form of projections of plans defined by the first-factor axis as adapted from Audigier et al. . Based on the coordinates of the farms on the main factorial axis, an Ascending Hierarchical Classification was used to group the farms according to their proximity to one another as adapted from Kouassi et al. . Descriptive statistics were used to summarize socio-demographic characteristics as well as other quantitative variables of the poultry farms surveyed. Chi-square test was used to test whether there is a significant difference or not in the farmers’ socio-demographic characteristics across regions and also if guinea fowl production indices differ across regions or not.

Non-parametric Kruskall-Wallis test was used to compare the means of guinea fowl production indices across regions. The significance threshold adopted was 5%.Guinea fowl rearing in the study area was largely dominated by men while very few women were involved as shown in Table 1. In terms of educational qualification, about one-third of the sampled farmers were illiterate while 36.4%, 26.1% and 3.0% had primary, secondary and tertiary education respectively. In addition, Atacora and Alibori regions had more illiterate farmers than the remaining eight regions. The highly educated guinea fowls farmers were found in Collines, Atlantique, Mono and Zou regions. There were no religious discrimination in the rearing of guinea fowl in the study area as it cut across all the religious groups in existence in the study area. However, it was prominent among christians than the other two religious groups. Other livelihood activities than guinea fowl farming among the respondents include crop farming , trading and art and craft . Specifically, of all the regions surveyed, the Alibori region had the lowest number while Atacora had the highest number of guinea fowl farmers.The cumulative contribution to the total inertia of the three selected factorial axis for hierarchical clustering was 92.88%. The frequencies of the different variables relating to the 4 categories of farmers are presented in Table 8. Cluster one was made up of 16.97% of the farmer sampled and were mainly located in the Alibori region. Most of the farmers were illiterate, that is, had no form of formal education and majority also had a minimum of 20 years experience in guinea fowl farming. The farmers in this cluster employed natural method of egg incubation and no additives were added to the birds’ feed and water to combat heat stress. The main and secondary activities of these farmers were livestock farming and crop farming . Extensive method of farming was largely employed by farmers in this region. Cluster 2 involved 25.45% of the farmers sampled. Most of the farmers had a minimum of 20 years farming and were located much more in the region of Atacora and Donga . The farmers in this group were mostly men. They were mostly illiterate and relatively young, as their age ranged between 25 and 50 years .

The average number of eggs used in setting up their farm was between 10 and 50 eggs. In terms of heat stress management by farmers in this cluster, only 7.1% of the farmers used additives in the birds’ drinking water. Most of the farmers engaged in cropping as their main activity and animal husbandry as a secondary activity. Furthermore, 69% of these farmers had shelters for their birds to protect them from harsh weather condition and rest. This feature made them to rear their birds under a semi-intensive system of production. Eggs were also incubated naturally by farmers in this group. Cluster 3 was made up of 24.85% of the farmers sampled. The farmers were mostly men and had primary education . More than half of the farmers in this group had relatively little farming experience in guinea fowl production. Majority of these farmers engaged in animal production as their main activity whereas 73.2% made crop production their secondary activity. The majority of farmers in this group did not add any additive to the feed or to the water of guinea fowl to manage heat stress. Cluster 4 in comparison to other groups, involved 32.73% of the farmers interviewed. These farmers were found mainly in the region of Collines , Atlantique and Zou . They were mostly male and between 25 and 50 years of age . Very few had no formal education with 37%, 44.4 % and 9.3% each having primary, secondary and university education respectively. The production system was largely semi-intensive in nature with very few running an intensive system. Animal husbandry was essentially the secondary activity of farmers in this group while crop farming was their main activity. Artificial incubation was practiced by very few of the farmers.The predominance of men in guinea fowl production as deduced from the study may not be unconnected to the difficulty involved in managing guinea fowl due to the wild instinct that guinea fowl exhibits. This instinct,dutch buckets system which is not very malleable constitutes a major constraint for the women in its production. This finding aligns with the earlier studies carried out in Ivory Coast ; Burkina-Faso and Ghana that found that guinea fowl farmers were predominantly men. Similar findings were reported in Togo , Niger and Zimbabwe . More attention on gender is required in policy design since women’s engagement in integrated farming is undeniable. This same observation is common in most African countries.

The study therefore emphasized the design and translation of technical manuals into local language due to the farmers’ low level of formal education as this will facilitate better understanding of the birds management requirement and subsequently lead to a sustainable development of this sector. Guinea fowl farming however does not present any religious and cultural limits that could hinder its production in rural areas as deduced from the study. This agrees with the earlier submission of Kone et al. and Kouassi et al. . Guinea fowl farmers engaged in other activities to diversify sources of income to meet up with the needs of their families. This result is in agreement with the findings of Traore et al. . The age of the farmers were between 25 and 50 years which was similar to the earlier report of Traore et al. that young people under 20 did not have enough resources to go into guinea fowl production. Farmers received more support from their children which are the employees as family labor. To this end, these farmers take the opportunity to pass on their knowledge and prepare them to take over. Good experience must therefore be documented by reinforcing them with innovative practices to boost the sector to respond more effectively to food security within rural households and in response to climate change, as also evidenced by Fotsa .In the majority of farms surveyed, farmers preferred to use the local hen to guinea fowl for incubating eggs due to their good brooding traits. The use of hens in villages for hatching guinea fowl eggs has been widely reported in other previous studies . In addition, guinea fowl and hen farming are closely linked . Most often, hens are used to incubate the guinea fowl’s eggs. Sometimes farmers also make use of other poultry species such as duck and turkey for the incubation . The feed stuffs used in formulating the diets of guinea fowl in Benin included corn, sorghum, millet, fonio and sometimes rice.

These diets are supplemented in some farms with maggot and termites. This gesture of giving a few handfuls of these feed to the birds was done to train the birds so as to return to the farm after free-ranging. While on free range, guinea fowl also feed on insects and grasses, which further diversified their diet. Traore et al. in Burkina Faso and Kouassi et al. in Ivory Coast, reported that the feed stuffs used for guinea fowl comprised sorghum, millet and corn. These results are different from those obtained in India where rice is the main diet offered to guinea fowl. These findings show that the choice of feed be offered to guinea fowl depends on the local availability and accessibility of the ingredients. The drinking water of guinea fowls was mostly non-drinkable. The lack of drinking water combined with the relentless distribution of mainly energy feed and kitchen waste to guinea fowl constitute factors that reduce the productivity of guinea fowl in rural areas. This same observation has been reported in Togo through the work of Lombo et al. who submitted that guinea fowl receive mainly energy feeds, thus affecting their productivity. Guinea fowl farming was intended to provide additional income to households while providing them with low cholesterol meat . These households were also concerned about the sustainability of the sector by investing in the continuity of the flock. An earlier study reported that guinea fowl production contributes to the cultural and religious ceremonies of certain sociolinguistic groups, including the annual Ditamari festival . This study revealed that bonds of friendship and fraternity are strengthened through the use of guinea fowl as gifts to friends and also, in recognition of services rendered.

GHG emissions from different farming systems must be urgently constrained because of the expanded production of mariculture

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

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

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

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

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