Horticulture

From this perspective, economists often propose that farmers should pay for the real value of the water they use for irrigation. Because of the presence of subsidies, the real value of water in agriculture is different from the price paid by farmers.It is also different from the cost of delivery, which does not account for the value of water itself. However, what is the real value of water? In the absence of a market and associated market prices, the answer to this question is not straightforward.This paper developed an approach to determine the value generated by irrigation water as a factor of production in agriculture; using biophysical models we estimate the increase in crop production afforded by irrigation with respect to baseline rain-fed conditions. These estimates are then used to determine the maximum price that farmers would accept to pay for irrigation water.They may be excessive when they do not account for output subsidies or cost of investments in irrigation technology, the operating costs of irrigation due to additional labor and energy, and other inputs . In some cases, the annualized cost of irrigation infrastructure,square plastic pot and the maintenance, and operation costs, can be so high that they exceed the water values we have determined .

However, these costs are typically subsidized by the government and farmers only pay for part of them . Thus, our approach would still determine the value of water to the farmers and provide a maximum reference value at which farmers might accept to sell or relinquish their water rights or water allocations to other businesses. The cost of irrigation infrastructures, their maintenance and operation are in general difficult to estimate on a global scale because of lack of data. Whether the assumption ΔPC ≈ 0 is justifiable likely depends on a number of factors, including crop type and value, farm size, irrigation technology, and irrigation water source and its distance to the field. Global datasets for a worldwide validation of this assumption are not available. However, in Australia, the Bureau of Statistics reports the total cost of irrigation by region, including the costs of equipment, infrastructure, water license, and operation, in addition to data on irrigated areas and agricultural water withdrawals . A recent analysis of these data has provided annual water provision costs per unit area of irrigated land. We can use these results to estimate the annual cost of water provision per unit volume of irrigation water and compare them to our estimates of the value of water. We find that provision costs typically range between US$0.01 and $0.02/m3 . While in the case of some staple crops such as wheat or maize the value generated by irrigation is comparable , these water provision costs are overall negligible with respect to the average value of water in agriculture in Australia, which is here estimated at ∼$0.27/m3 , in agreement with estimates based on irrigation and production data from the Australian Bureau of Statistics . As noted below, these values fall within the interval of water prices reported by the Murray– Darling water market, suggesting that in the case of Australia the assumption of negligible provision costs can be overall justified. The values of water found by this study fall within the range of those reported for water markets. In fact, we find median global values of $0.13/m3 , while it has been reported that in 2012 Colorado farmers typically pay $0.02 to $0.08/m3 for irrigation water .

However, in the presence of competitors from the oil industry, farmers are outbid in water market transactions at prices ranging from $0.81 to $1.62/m3 in periods of water scarcity . This suggests that, when demand from another industry that is willing to pay a higher price for water comes into play, farmers sell their water rights if the price exceeds the value of water in agriculture. Our study provides an estimate of such a value. For instance, in 2012 in the case of maize, the value of water in the United States is here estimated at $0.25/m3 , in general agreement with water trade data. For instance, in the Permian Basin in Texas, a computer application has been developed that connects owners of water rights to oil companies and allows them perform water transactions. While farmers would normally pay $0.05/m3 , in periods of water scarcity, competition with shale oil and gas companies have brought the price to up to $2.50/m3 . Overall, in the presence of water markets and demand from oil companies, water prices may increase from $0.03–$0.1/m3 to $2.3–$3.1/m3 . Thus, in the presence of competition with other sectors capable of providing a more efficient use of water the market price of water increases and farmers are outbid by oil companies at prices exceeding the agricultural water use efficiency determined by our study. In the Murray–Darling’s water market the median water price reported between 1998 and 2015 varied between $0.05/m3 and $0.50//m3 in periods of scarcity . These values are of the same order of magnitude as the value of water in agriculture we have estimated for the Murray– Darling Basin . Thus, in this study we calculate the value produced by water in its current use. The general pattern that is observed in the analysis of the economic water efficiency of water consumption is that with the current crop distribution the value of water in agriculture does not necessarily correspond to the “best use” of water because it does not exhibit the maximum water value and is at least one order of magnitude less than that in other sectors . Therefore, in the presence of a water market water consumption is expected to shift from irrigation to the activity that maximizes revenue generation.

However, the focus here is on the estimation of water values in agriculture not on how different uses can compete with one another. In the case of water, such a competition is often limited by the lack of a market as a result of institutional or physical factors . While water markets give a direct assessment of the value of water resulting from the complex interactions among different sectors, their existence is limited to those cases in which suitable trade able property rights have been established. For the rest of the world the value of water can still be determined through its ability to produce value in different economic activities. Our results provide a first global estimate of the value of irrigation water worldwide.Fossil fuel combustion and land use change are contributing to alterations in global climate. Atmospheric [CO2] has recently increased at an average rate of 2.0 μmol mol−1 year−1, which is higher than any measurement period to date over the past 800 000 years . Barring substantial reductions in emissions, atmospheric [CO2] may exceed 900 μmol mol−1 by the end of the 21st century 8.5; IPCC 2013). Rising atmospheric [CO2] has contributed to significant atmospheric warming, and global mean surface temperatures could increase 3 to 4 °C by the middle of the century . Embedded in this climatic warming trend is an increased frequency of extreme temperature events . Atmospheric warming has also been implicated in more frequent and extreme precipitation and drought events , and net declines in soil moisture in many, but not all, regions. Temperature, water availability and atmospheric [CO2] are each important regulators of plant growth, function and development. Thus, climate change will likely influence the ability of agricultural systems to meet a growing global population’s demands for food and fibre. It is expected that food production must increase 70 to 100% by 2050 to meet growing demands . Troublingly, recent trends suggest that yields are not increasing rapidly enough , and climate change and extreme weather events may already be reducing crop yields in some areas . For instance, Australia suffered enormous losses in wheat yield during historic drought in the early 21st century and higher than normal temperatures have contributed to reductions in corn and soybean yields in the United States between 1982 and 1998 . Even with high precipitation, higher temperatures can increase evaporative demand and reduce soil moisture resulting in greater incidence of drought . A recent analysis of maize and soybean yields in the Midwestern United States showed that although field-scale yields are increasing, they have become increasingly sensitive to drought . Alternatively, experimental manipulations have demonstrated that elevated [CO2] can stimulate C3 crop yield ,tall pot stand yet the magnitude of this increase is uncertain under field conditions where temperature and precipitation can influence the CO2 fertilization effect .

Climate change also threatens the ability of forests to meet global demands for wood products . Although forest plantations only account for 5% of global forest cover,they supply roughly 35% of global round wood, with future wood production expected to increase in plantations compared to native forests . Concentrating wood production to smaller areas promotes greater forest protection and mitigation on non-plantation lands , but necessitates sustained productivity over time, which will become more difficult under extreme climatic conditions. Experimental studies have shown that forest trees and plantations may increase productivity under eCO2 , although variability in the growth stimulation is dependent upon variation in soil fertility, temperature and precipitation . Atmospheric warming is generally expected to increase tree and forest growth in cool climates, but have no effect or reduce growth in warm climates . In addition, there is mounting evidence that more frequent and intense heatwaves and drought are leading to lower tree growth rates and increased tree mortality in some regions worldwide . Although fast-growing mono-specific plantations have high rates of C accumulation , lower stand-level genetic diversity may increase their susceptibility to heatwaves and drought stress . Utilization of intraspecific variation in agricultural and forest species responses to climate change may bolster productivity and aid development of greater stress tolerance or resilience . Genotypes of a given species often show markedly different physiological, growth and developmental responses to eCO2, temperature variation and soil water availability, exemplified by genotype-by-environment interactions . Careful examination of genotypes’ plastic responses may reveal individuals that can both increase productivity under optimal conditions, and, in part, sustain production under stressful conditions . Despite the potential utility of intraspecific variation in agricultural and forest species responses to climate change, an integrative understanding of the physiological and genetic factors influencing G × E is lacking, and relationships between genotype plasticity and productivity have rarely been tested in the context of agriculture or forestry. The goal of this paper is to link aspects of plant physiology and genetics that may influence intraspecific variation in agricultural and forest species responses to climate change. In particular, we: conceptualize the importance of intraspecific variation in agricultural and forest species phenotypic plasticity within the context of plant breeding and climate change; highlight some physiological mechanisms underpinning intraspecific variation in agricultural and forest species responses to drought, warming and eCO2; discuss the genetic factors influencing intraspecific variation in phenotypic plasticity;and discuss future directions in G × E climate change research.G × E can take different forms including plasticity arising from changes in variance among genotypes across environments, and plasticity resulting from genotype rank changes among environments . Genotypes may also show variable linear or non-linear responses to continuous environmental variation , which may be important for identifying response thresholds to environmental drivers . A genotype’s plasticity is often indexed based on the slope of its ‘reaction norm’ across an environmental gradient , where steeper slopes represent higher plasticity. A long-standing focus in plant breeding has been to measure and utilize information gained from G × E. Typically, the goal in plant breeding is to produce genotypes that are productive across a range of environments or management conditions . Significant G × E, especially in the context of increasing variability of E, hinders selection of stable genotypes . Plant breeders have therefore attempted to limit G × E by prioritizing genotype stability across environments . Alternatively, breeders have also focused on selecting genotypes with improved productivity or stress tolerance in particular environments . From an ecological and physiological perspective, however, and in the context of climate change, careful examination of G × E and its underlying physiological and genetic mechanisms could be important for identifying genotypes suitable for increased climatic variability.

Managing water quality in river and ground water ecosystems is another shared challenge for sustainable agriculture in both the U.S.and South Korea.Water quality is intrinsically tied to water storage levels, stream flow and climate change.When estimating future life-cycle eutrophication, Lee et al.found that eutrophication in the Midwest U.S.stays relatively steady when using the Representative Concentration Pathways , developed by the Intergovernmental Panel on Climate Change , except in the scenario where GHG emissions are high.High levels of GHG emissions from corn production in the Midwest coupled with ambient temperatures and precipitation suggest a sharp increase in eutrophication in the region by 2022 for a four-year period and then again in 2057.In South Korea, recent economic activity and the influx of pollutants have increased, therefore, as preventive methods, standard fertility prescription, non-point pollutant control, organic farming with low energy, and livestock manure cycling have been implemented.The Rural Community Corporation, which supplies the right amount of high-quality water required for farming in a timely manner by managing agricultural reservoirs, pumping stations, and water canals, has been monitoring water quality in real-time through automatic water quality measurement devices, hydroponic nft channel predicting water quality changes through big data and artificial intelligence analysis and conducting preventive water quality management.In 2020, the corona virus significantly disrupted the supply and demand cycle for agricultural products and disrupted agricultural distribution systems in both countries.

The decline in food demand by restaurants and hotels coupled with reduced demand for bio-fuels as travel decreased had an immediate and severe impact on U.S.farmers and resulted in lower crop and livestock yields and a disturbance in the food supply chain.In South Korea, sales for in-person walk-in food markets dropped by 19.6%, and online sales increased by 46 %.In both countries, food and horticulture exports were down due to global cancellation of events.Food service providers, food catering companies and farmers were severely impacted from school and restaurant closures.The overall projection by the Organisation for Economic Co-operation and Development is that the impact of the COVID-19 pandemic will have ongoing effects throughout the next decade caused by a decline in consumer demand, and disruption in agro-food trading and the downstream food processing industry.The demographics of farmers in both countries indicate an aging workforce and a shrinking rural population.The average age of U.S.producers in 2017 was 59.4 years , with only 9.4% of producers being 35 years old or less.Prior to the COVID-19 pandemic, unemployment in non-metro areas had begun to decline and there was a slight increase in rural populations.The upturn was due in part to better labor market conditions and recovering real estate markets in rural areas.Nonetheless, more than 82% of the nation’s population continues to be concentrated in big cities.South Korea has a similar situation with most producers being 65 years or older.South Korea is also experiencing a population decline in rural areas.The rural population in 2018 was 18.54 % of the total population , which represents a decrease of 84.4% as compared with its rural population in 1970.The aging and decrease in population are due in part to urbanization and most younger citizens leaving for cities where the living standards are higher ,and agricultural mechanization.

According to Yoon et al., in addition to the problems of an aging farmer population and reduction in farmland, the free trade agreements with the European Union, China, and the U.S.have weakened the competitiveness of domestic agriculture.A summary of challenges to agricultural production are listed in Table 2.The U.S.and South Korea are known for their innovative technologies , which carry over to the agricultural sector.Historically, both the U.S.and South Korea were dominated by an agrarian culture, but now both have mixed economies.American agriculture began to experience a significant change in the early 1900’s transforming from a labor-intensive sector to highly efficient mechanized operations.South Korea quickly transformed to a leading economy in a single generation , in part due to comprehensive five-year economic plans developed by the government and investment in social overhead capital in the technology sector.The high degree of innovation and embracement of advanced technologies, serves both countries well in their quest towards smart solutions.Currently, both the U.S.and South Korea are working towards the development of smart farming systems or elements of smart farming to adapt to and mitigate the challenges posed by limited resources, climate change and environmental impacts.The U.S.passes legislation every five years, commonly known as the “Farm Bill”, to address national agricultural and food policy.The current farm law applies through 2023.Policies are carried out through a variety of programs including nutrition, crop insurance, commodity support and land conservation.While the farm bill authorizes and pays for mandatory expenditures and establishes limitations for discretionary programs, a national American approach to develop a smart farming system does not exist.Rather, advances in agricultural technologies and information systems that constitute elements in smart farming systems have been or are being developed mainly by the private sector, although public non-profit companies, and university institutions have had a role in agricultural innovations.

In more recent years, corporations that invest in agricultural R&D are prone to mergers and acquisition.Smart farming solutions designed in the U.S.are mostly hardware or software products that can operate independently or in combination to provide farm management processes.Examples are GPS-guided tractors, yield monitors, variable rate sprayers for pest control, planters and variable rate fertilizer implements.All these technologies have been widely adopted in the U.S., mainly because this equipment allows farmers to manage large-size farms more efficiently and optimize more precisely the inputs with no additional human labor.Currently, in the U.S., smart system products developed by private industry are available to farmers on the retail market.Universities and the Agricultural Research Service are also involved in developing smart farming solutions for precision irrigation management in collaboration with private industry or with state cooperative extension specialists.specific smart system solutions include automation and equipment control , optimization of machine operations , or provision of decision support tools for irrigation scheduling, forecasting precipitation, or developing variable rate application maps for fertilizer or irrigation.The market for smart hardware also addresses the need to reduce the time that a grower spends monitoring and making agronomic decisions for large-size fields or for multiple fields.Decision support algorithms are data driven and typically based on any one or a combination of in-situ sensors, image sensors, imagery from UAVs or satellite systems in combination with edge or cloud computing and machine learning algorithms.Information is acquired by farmers using mobile phone apps or web-based computer sites.In many cases irrigation companies are working with software firms and tech companies that offer geo information services to provide a whole package solution.The shortfall of these smart hardware and software solutions are that they often use unique algorithms with proprietary platforms to limit their compatibility between manufacturers.Non-profit groups also play an indirect role in driving the development of concepts and elements of smart farming in the U.S.

Examples include the Council for Agricultural Science and Technology , a nonprofit organization, that provides information to policy makers, the media, private industries and the public.The CAST group developed a position statement on Climate Smart Agriculture that emphasizes the role that agriculture can play in helping address climate change while creating jobs and economic opportunities.In addition, Ag Gateway, a global non-profit organization is helping to frame smart farming on a national scale in the U.S.Its mission is to develop resources and relationships that drive digital connectivity in global agriculture and related industries.In working with the American Society of Agricultural and Biological Engineers , AG Gateway pushed for the development of data exchange standards for transaction and electronic data compatibility.This initiative was meant to standardize language and improve data exchange across multiple hardware and software platforms to enable interoperability among sensors and equipment used in precision irrigation technologies.Use of the standard by manufacturers and industry members is voluntary.A summary of the main smart farm concepts for the U.S.is listed in Table 4.In South Korea, concepts for smart farming solutions are more holistic.The Korean national innovation system was implemented to develop regional economies based on technological innovation.The system emphasizes the role of government in leading collaborative research and development to promote technological capabilities and is perpetrated in the agricultural sector with the dominant purpose being rural economic development.In the arena of smart farming, the Korean government aims to improve productivity and quality by enhancing ICT utilization through education,nft growing system consulting, and follow-up management.The Korean government views smart farming as a system to help guarantee the generational sustainability of agriculture, it is determined to change the national agricultural structure to meet the trends and demands of the times, such as digitization and low-carbon conversion.The Korean government also envisions smart agriculture as a mean to continue to regenerate rural areas as the core idea of the Korean version of the New Deal.Smart farming, which combines ICT and robot science technology such as big data, artificial intelligence, and the Internet of Things , is spreading and disseminating to respond to the devastation to the agricultural environment caused by climate change and solve the agricultural problems.

As part of these efforts, the Ministry of Agriculture, Food and Rural Affairs has been promoting agriculture for the purpose of upgrading agriculture, responding to the aging of farmers and nurturing young farmers.MAFRA has set an expansion target by 2022 and is promoting ICT convergence projects in agriculture , development of Korean smart farm models, and R&D support projects 2019.The goal was to enable 7,000 ha of farms and orchards, and 5,750 barns to operate as smart farms and smart operations, respectively, by 2022.Since 2018, for the spread and advancement of smart farms, the creation of a youth startup ecosystem, establishment of industrial infrastructure, and creation of a smart farm innovation valley are being promoted as major policy tasks.The Rural Development Administration of the Republic of Korea has been concentrating its research capabilities on securing key elements and source based technologies to develop the world’s best Korean-style smart farm model, and to make the entire process of the perch production system smart.The Korean smart farm project is a long-term project to secure independent agricultural production technology that can compete with advanced agricultural countries by developing technology suitable for agricultural environment and field conditions without importing, applying, or simply imitating foreign advanced technology.This Korean smart farm prototype follows a technology model with various levels : 1st generation-improved convenience with remote monitoring and control, the 2nd generation-improved productivity through intelligent precision growth management, and the 3rd generation-export of smart farm integrated system such as energy optimization and robot automation of the technology are developed and put into practical use.The project plans to reduce the use of labor and agricultural materials, link it with farm household income through productivity and quality improvement, and further solve the difficulties in the farming field and related industries at the same time.Currently, because the ICT devices being distributed are not compatible with each other due to the different product specifications of each company, the integrated management and maintenance of smart farms is difficult.Accordingly, ICT equipment standardization and other standardization work are underway to unify the format and communication method into one common standard for various sensors and controllers used in horticulture and livestock.While South Korea emphasizes smart farming communities, the government also embraces discrete smart farming solutions in the form of smart agriculture equipment blended with the idea of digital agriculture which combines ‘precision agriculture’ technology with intelligent network and data management and utilizes big data and artificial intelligence for decision support.Smart farm applications are currently being used in greenhouse production and field production.The main smart farming concepts for Sourth Korea are summarized in Table 4.Smart farming has the potential to reduce labor and increase efficiency of agricultural inputs and time management for producers, this would benefit both countries.Reduced inputs with limited reduction in quantity and quality of yield could translate into profitability.Sector growth is envisioned as long as the ICT system affords data strategies providing intelligent information and services to farmers such as potential buyers for their products and predictions for future demands.The global market for smart agricultural goods was estimated at 6.34 billion USD in 2017; this market is projected to reach 13.50 billion USD by 2023.

The patterns of adaptive capacity, however, are biased because its factors of farming economic status and innovative capitals are both affected once metropolitan counties are removed.Overall, after excluding metropolitan areas, vulnerability remains the same with notably high rates in the northwest and southern margins of Iowa, and lower rates in northeast Iowa and central Iowa comparing Figs.6 and 9.To calculate the overall vulnerability, this study simply merged index scores of sub-components of extracted factors.There needs to be more effort in selecting, weighting, and normalizing indicators that can influence the vulnerability estimates alone.When selecting initial variables, this research incorporated responses on winter storm impacts and adaptation from a limited number of farmers, which may not well represent local perceptions for the entire state.To make the sample more representative and vulnerability metrics more context-specific, more respondents may be considered based on sub-types of farms.The number of extreme days, such as the average number of days with a maximum temperature greater than 90 percentile was used to estimate exposure.In our case, the number of consecutive cold days may be selected to measure the exposure to winter storms in future studies.To establish a vulnerability index,vertical rack system sub-indices may be developed to achieve relative weightings.For example, Antwi-Agyei et al.incorporated a crop yield sensitivity index and an exposure index to calculate the vulnerability to drought.

In terms of normalizing, Hahn et al.calculated index scores for major components considering the weight and the number of indicators, resulting in overall vulnerability ranging from − 1 to 1.Finally, it should be noted that the selected indicators derived based on interviews with farmers in Iowa may not apply to vulnerability assessments in developing countries, considering agricultural regions in these regions are more likely to be severely affected by extreme climate events and the associated rising food prices.Further validation for the vulnerability pattern can be done using surveys investigating farmers’ perceived vulnerability and on-farm losses from winter storms in different counties.Addressing land degradation has been highlighted as a key objective to ensure a sustainable future and given about a third of the worlds ice-free land is farmed , agricultural systems will play an integral role in meeting such objectives.Ensuring the sustainability of agricultural land requires approaches which restore ecological functioning and resilience whilst at the same time contributing to global food security and supporting the health, safety and well being of farmers and farming communities.This is no easy feat, and some argue that a sustainable future requires a paradigm shift in how agricultural systems are managed, with many advocating for a transition from productivist approaches to agriculture, to the adoption of a socio-ecological systems approaches.A socio-ecological systems approach views the farm as a dynamic system of interdependent components , and farm goals are set in relation to the functioning of the farm as whole, rather than in relation to individual components of the system.This involves setting farming objectives that aim to achieve not just agricultural production goals, but also ones related to ecological sustainability, long-term systems resilience, and human well being.Recent years have seen an increase in popularity of socio-ecological systems-based farming approaches, with more farmers world-wide adopting these types of approaches to farm management.

However the effectiveness of these systems in achieving sustainability goals is the subject of much debate, with different studies reporting varying findings, along with ongoing argument about what types of socio-ecological approaches may in fact have the benefits for sustainability claimed by their advocates.Some argue that the conflicting research findings on this topic are due to the complexity and nature of socio-ecological farming systems, which involves the implementation of an individualised farm management approach relevant to each farmers’ specific circumstances, rather than the implementation of specific “sustainable practices”.We believe it is likely to be at least in part a result of how these studies define these farming systems: most research has measured socio-ecological farming systems based on the adoption of practices commonly used in these systems rather than management characteristics.This may explain some of the inconsistency in findings observed in contemporary literature.Analysing socio-ecological systems is also challenging for other reasons, namely that causal relationships within the system can be difficult to identify using conventional statistical methods.Farming according to a socio-ecological systems approach involves “recognizing and synthesizing components and patterns of a system that are interconnected, which interact, and which combine to encompass a complex and dynamic whole” , p.2.From this perspective, indications that a system is functioning well are less about the extent to which each individual component is functioning, and more a product of how well the components of the system are working together as a whole.Identifying cause and effect relationships between components of the farm is therefore difficult, if not impossible, since the aim of the system is for all components to interact in dynamic ways.Perhaps due to these challenges, many studies examining social outcomes of regenerative farming have used qualitative research methods.Although qualitative approaches offer a useful insight into the effectiveness of these systems, quantitative assessment offers insights into the effectiveness of these systems that differ to those possible when using qualitative methods.

Particularly, they enable identification of how common it is for particular benefits to be evident, and the ability to compare this for large numbers of farmers.Given the popularity of socio-ecological systems, there is a critical role for quantitative as well as qualitative assessment, in order to better evaluate whether and under what circumstances they can address land degradation and other environmental issues.Understanding socio-ecological systems as a management approach, rather than the application of specific farming practices has not only made evaluating the effectiveness of these systems difficult, but also made the establishment of a shared definition of what socio-ecological farming systems are problematic.Regenerative agriculture is one of the most common terms used for socio-ecological farming systems, and for the purpose of this paper we will use regenerative agriculture as an umbrella term that encompasses all types of socio-ecological agricultural systems, including holistic management, low-input farming and biodynamic farming to name a few.By doing so, we support the view that regenerative agriculture is not defined by the application of particular practices, but rather the a philosophical approach to farming which incorporates specific principles that are shared across all socio-ecological systems regardless of the label attached.These principles include incorporating natural systems into farm production systems and undergoing a continual process of evaluation and adjustment to farming practices based on on-farm observation, learning and monitoring.Proponents of regenerative agriculture claim that it is this philosophical approach to farm management that leads to positive impacts on the social, financial and ecological functioning of the farm, proposing it to be a valid, and sustainable solution to land degradation.As such, managing the farm as an interconnected system is considered by many to be the key characteristic of regenerative agriculture that makes this approach a sustainable one.This means that evaluating the sustainability of regenerative agriculture requires development of indicators that are not only indicative of the farm functioning as system, but are also relevant to the many different approaches and practices regenerative farmers may adopt when implementing a systems approach appropriate to their individual circumstances.Further, fuelling this debate is acknowledgement that identifying sustainability indicators suitable for regenerative farming systems is needed as part of developing a more robust body of evidence regarding the effectiveness of these approaches for environmental and human well-being.

While developing sustainability indicators for a type of farm management defined by a shared philosophical approach rather than the use of specific farming practices may seem counterintuitive, we argue that understanding regenerative farming this way is critical to the development of sustainability indicators that are appropriate and relevant to the diversity of practices adopted by farmers operating under this framework.Doing so acknowledges that achieving sustainability goals will depend on the extent to which the whole-of-farm system is orientated toward regenerative principles, and that the extent to which a practice is deemed to be sustainable will vary depending on the characteristics of the farm system.For example, a commonly used practice among some types of sustainable farming is to cease use of all synthetic fertilisers.While this may be indicative of a sustainable farming system, if synthetic fertilisers are simply replaced by extensive use of ‘natural’ fertilisers, there may still be a negative impact on the environment rendering the system unsustainable.Selecting sustainability indicators based on principles rather than practices helps to overcome this issue.Past studies report that regenerative farming involves explicitly setting farming objectives based on consideration of how to improve well being and quality of life,mobile grow rack and that farmers’ understandings of what it means to have good well being often changes when switching to this type of farming.Regenerative farming philosophy recognises that the well being of the farmer does not rely solely on the production value of the land but is also influenced by social and ecological factors.More importantly, good quality of life is considered by regenerative farmers as an explicit objective that is achieved through having all components of the farm functioning well as a whole.As a result, farm-based management, planning and goal setting is centred around creating a farming system that supports the well being of the farmer, rather than focusing solely on the production aspects of the farm to meet farmer needs.It is this mindset and management approach that proponents of regenerative agriculture believe enables them to establish a farming system that is better able to withstand key occupational stressors such as drought, irregular rainfall and pest/disease outbreaks.Recent years have seen growing recognition of the importance of human well being in achieving sustainability outcomes, with the United Nations Sustainable Development Goals declaring “If a farm is not economically sound or not resilient to external shocks, or if the well being of those working on a farm are not considered, then a farm cannot be sustainable”.This shift signifies a change in how we think of ‘sustainability’, with some suggesting that sustainability and well being can be viewed as ‘twin concepts’, united by a shared goal of improving well being.

While many definitions of well being can be found in the literature, there is general agreement that well being is related to “the presence of positive emotions and moods , the absence of negative emotions , satisfaction with life, fulfillment and positive functioning”.well being can be measured indirectly through its determinants such as education and income – often referred to as objective indicators of well being – or through subjective indicators which assess well being directly as a social, psychological and emotional experience.Despite farmer well being being recognised as central to the effective implementation of regenerative farming systems at the local level, efforts to evaluate the effectiveness of these systems seldom include aspects of the farmers life as an indicator.Often, research into assessing sustainability outcomes of regenerative agricultural systems focus on outcomes at the global or national level , or limit assessment to either environmental or economic outcomes.There is growing recognition that social factors should be incorporated in frameworks assessing the sustainability of agricultural systems , however often the indicators used are relatively narrow and measure determinants of well being such as housing, education and health , rather than well being itself.Although such indicators may be suitable proxy measures for well being in some circumstances , they may not be for regenerative agriculture, where well being outcomes or improvements may not involve changes in housing, education or physical health.Suitable measures need to be identified that can robustly and appropriately examine whether engaging in regenerative farming is associated with change in well being, and that can reflect change in quality of life experienced by the farmer over time.While there is reasonable consensus that understanding the well being of a particular group can be best achieve through combining use of both objective and subjective measures of well being , in the specific case of socio-ecological farming systems the use of objective measures of well being may be inappropriate for two reasons.Firstly, the adoption of socio-ecological farming systems requires a farmer to question some of the tenets of conventional agriculture such as those which focus on increasing production and income , and instead focus on goals which serve the functioning of the system as a whole such as matching production levels to the natural capacity of the landscape.This can lead to a reduction in income for farmers following a systems approach.

Alternatively, information providers can strategize with farmers on how to deal with inevitable trade-offs.Additionally, information providers that find it challenging to generate feedback from farmers based on their mode of WIS delivery may consider using a selection strategy that enables different categories of farmers to be selected across the district to give their opinion about the WIS.Furthermore, we found that the origin of WIS played an essential role in WIS usability in the study district.The origin of WIS as defined by farmers was not about the physical space per se; instead, it was more about information providers and establishing personal bonds with farmers.As a result, farmers had some sense of security in the reception of WIS because they knew who to contact if the information ‘failed’.This finding resonates with other studies,blueberry grow pot which indicated farmers’ preferences for informal means of interacting with information providers located in the farming communities.Findings on the unmet need of information providers originating from the farming communities are similar to other studies which have also reported that the disconnection between information producers and farmers is a constraining factor for the uptake of climate information.In this instance, we observed preferential choices in WIS usability.It can be trusted and applied if WIS comes from the farmer/farming community, the private weather forecaster, AEAs, and Radio Ada.

Based on the finding on the origin of WIS, it can be inferred that, in the study district, if an information provider can position themselves as a trusted person, it enhances WIS usability.This approach can be a positive characteristic, but it can equally pose a challenge when the provision of WIS is associated with an individual.This is because associating WIS usability with an individual bears the risk of creating dependencies that can destabilise information delivery in that person’s absence.The private weather forecaster’s WIS may not be sustainable in the long run because the providers’ absence will bring an end to the delivery of this information.Similarly, the sustainability of AEAs’ WIS could be affected by a change in extension staff.Farmer-to-farmer WIS may gradually disappear when experienced farmers are no longer present, given that their information is rarely documented but rather exists as tacit knowledge.Regarding this finding, we propose that information providers build farmers’ trust towards a WIS rather than focusing on building personal bonds.Poverty and hunger continue to be the most pressing issues facing the development of many nations around the world, particularly in the less developed regions such as Sub-Saharan Africa.SSA remains the world’s most food-insecure region, with nearly a quarter of the population , suffering from malnutrition.

As a global goal, the 2030 agenda for Sustainable Development has recognized the significant consequences of rising food poverty which requires urgent attention.According to the World Bank , poverty is defined as a multifaceted notion that includes low income and consumption, poor educational accomplishment, poor health and nutritional results, a lack of basic services, and a hazardous living environment.To categorize households based on the different levels of poverty, a poverty line of US$1.90 per day is used as an indicator of extreme poverty.Many of the extremely poor households live in rural areas and rely on agricultural production for a living .To improve long-term food security and alleviate poverty, agricultural production systems are expected to be more productive and reduce output variability in the face of climate extremes such as land degradation.Farmers’ productivity stability is linked to the adoption of a resilient food production system that can withstand disruptive events.Irrigated farming has been identified as a viable means of increasing agricultural productivity, farmers’ revenue, and household consumption as a mitigation strategy.Irrigation aids in the stabilization of food production by shielding it from the unpredictability of rainfall.Irrigation farming systems are a critical policy strategy for eradicating poverty and increasing food security.In addition, irrigation participation is especially crucial in import-dependent developing countries, where agriculture employs the bulk of the population.Irrigation projects and existing schemes, despite their importance in economic growth and investment, are still under-performing in terms of realizing their full potential, particularly in a semi-arid country like South Africa.

In South Africa, farmers’ participation in irrigation farming is generally low, with smallholder irrigation land area accounting for around 0.1 million hectares of the aggregate irrigated land.Despite the importance of smallholder farmers to the South African economic development as they possess potential for improving the rural livelihoods, farmers participating in different irrigation schemes perform below sub-optimal levels.Water management, financing access, market access, poor infrastructure maintenance, and the farmers’ age have been found to contribute to low participation in irrigation farming in many developing countries.Christian et al.observed that irrigation participation in South Africa is influenced by farmers’ age, family size, financial availability, extension contact, and membership of farmer groups.While factors determining participation in irrigation farming has gained some attention in South Africa, the impact of irrigation participation on household welfare, poverty and vulnerability to poverty has been inadequately explored.As a result, any untapped potential to enhance household welfare and reduce household poverty level and vulnerability to poverty through smallholder irrigation participation in South Africa is critical.Many pieces of literature have reported that participation in irrigation farming could serve as a way to create new job opportunities, both on and off the farm, and boost rural incomes, improve livelihoods, improve food security and alleviate poverty, through improvement in farm productivity.However, while there is evidence that irrigation development reduces poverty in several countries, the impact is determined by farm, irrigated technology and household variables.For the reasons stated above, it is vital to investigate whether irrigation users are significantly better off than non-users in terms of not only poverty status but poverty incidence, depth, and severity, as well as the impact irrigation has on consumption levels.Moreover, plethora of empirical studies on poverty has one major shortcoming: the failure to estimate treatment on vulnerability to poverty.

It is critical to recognize the differences between poverty and vulnerability.The former is more concerned with one’s immediate well-being, whilst the latter is concerned with one’s long-term well-being.Thus, assessing poverty without considering vulnerability to poverty may result in insufficient information for future agricultural-related program design and implementation.Thus, there is little empirical literature on the impact of irrigation participation on an extended outcome such as household welfare and household poverty, as well as vulnerability to poverty.As a consequence, the study hypothesized that smallholder farmers who participate in irrigation farming have higher consumption expenditure per capita, a lower poverty level, and are less vulnerable to poverty than non-participants.This study brings out novelty in poverty-related studies in the following ways.First, we estimate the contribution of irrigation usage on not only poverty reduction but the incidence and severity of poverty as well as the vulnerability to poverty among farming households in rural South Africa.Knowing who is poor, the intensity of poverty and who is at risk of becoming poor is critical to inform farm-level policy initiatives and executions.Second, the study followed rigorous technique used by World Bank to measure poverty.Third, the study takes into consideration both observed and unobservable factors of irrigation participation through the use of the endogenous switching regression to account for selection bias and the potential endogeneity of participation in irrigation farming.Prior researches in the country are sparse in this regard, making it difficult to make conclusions.Changing social-cultural, political, and economic factors entail the need for up-to-date research findings on which to base the formulation and implementation of various programs to improve livelihoods.Through the provision of new empirical evidence,hydroponic bucket the study thus contributes to the efforts of government, international development organizations , and other stakeholders to strengthen and better understand the impact of irrigation sector reforms on poverty reduction and household welfare.This study was conducted in the King Sabata Dalinyebo and Nyandeni local municipalities which fall under the OR Tambo District Municipality, representing the local municipalities in the Mthatha River basin in Eastern Cape province.

The district is functionally rural, characterized by low educational levels and predominantly an agricultural producing area.The Mthatha River catchment has a dimension of approximately 100 km long and 50 km wide, with a total area of 5 520 km3.The Mthatha River, which is 250 km long and has two big tributaries, flows north of Coffee Bay.The Mthatha and Corana Dam, both on the Mthatha River’s Corana branch, are major water storage reservoirs in the Mthatha basin.The Mthatha Dam has an 886 km catchment area and can store up to 254 million cubic meters of water while producing 14.5 million cubics of water per year.A multi-stage sampling technique was employed for data collection.A purposively sampling technique was used to divide the catchment into four regions in relation to the source of the Mthatha River.These are the upper region, peri-township region, the lower region and the coastal region.In each of the areas, ten villages were chosen at random, of which 11 respondents were randomly chosen in each village based on their desire to participate in the survey.In total, 440 households were interviewed but only 400 were considered credible for analysis due to some uncompleted questionnaires.The study employed a quantitative method for the collection of data using a survey questionnaire.The survey questionnaire was prepared in English and then translated to a local language , as it is assumed that people feel more at ease speaking to others in their language, which improves the accuracy of information obtained and survey’s dependability.Following Dubbert, the quantitative method was used to compare responses between the participants and non-participants of irrigation farming because all respondents were asked identical questions in the same order to allow for significant comparison.The important sections of the questionnaire focus on respondents’ use of irrigation, farm activities, source of finance, water access and challenges associated with household food security.The questionnaire’s other major component was designed to find out about households’ demographic profiles and consumption patterns.The proportion of male-headed households in the participant and non-participant groups is 0.70 and 0.65, respectively.Farmers had an average age of 45 years, which is within the age range of the working population.The average age of participant and non-participant households was 46 and 45 years, respectively.This is similar to the average age of 52 years for Eastern Cape province found in the study of Akinyemi and Mushunje.As indicated in Table 1, the household size for the participants of irrigation farming is lower than the non-participants.A higher proportion of the participants of irrigation farming experienced flooded farms over the last 12 months preceding the survey compared with the non-irrigation participants.Furthermore, the statistics show that more respondents under the participants of irrigation farming category obtained income from livestock sales and also incur lesser expenses on education.This is similar to the study of Mwangi and Crewett who found that participation in irrigation farming was driven by years of education of the farmers.Participants in irrigation farming receive more financial support through remittances than the non-participants, with many of the participants preferring to engage in seasonal farming.The majority of the farmers who practiced crop diversification are irrigation participants, with most of the participants having more education years than the non-participants in the study area.The statistics result shows that leased and communal land were important variables for assessing irrigation farming participation, given that land tenure system, especially the communal land, prohibits the purchase/sale in South Africa, for instance, the case of KwaZulu-Natal.The treatment variable used in the study was irrigation farming and the result shows that about 45% of the households participated in irrigation farming while the remainder represents the non-participants.The information in Table 2 presents the summary statistics and description of the outcome variables, which are the household consumption per capita expenditure, poverty levels and poverty vulnerability.The food consumption per capita expenditure of households that participated in irrigation farming is significantly higher than households that did not participate in irrigation farming.This implies that households that participated in irrigation farming are more likely to increase their consumption per capita expenditure.Findings from the literature confirm that irrigation participants have greater potentials for more farm yields and income, which increases the level of household consumption.The poverty gap index variables show that participants in irrigation farming have a lower poverty gap index, indicating that households who practice irrigation farming have lower poverty status than the non-participants.In line with our findings, Beshir has found that participation in irrigation farming reduces poverty and increase food security in Ethiopia.

The samples of the cephalothoraxes and muscle segment were fixed with 4 % PFA for 24 h at 4 ◦C and then transferred to a graded ethanol series for dehydration, followed by treatment with 100 % xylene and infiltrating in paraffin.The sections were obtained and stained with conventional H&E staining according to the previous procedures.Subsequently, the sections were scanned through the PANNORAMIC Pathology Diagnostic Scanners to obtain good quality images.In situ hybridization was performed on serial tissue sections.In briefly, the sections were dewaxed in xylene, followed by rehydration with successively dilute solutions of ethanol.Then ISH using CMNV as a probe was performed on three sections according to the protocols described previously.After color reaction, counter staining of the sections was carried out by using the Nuclear Fast Red solution , followed by dehydration in alcohol and mounting with water-soluble sealant.Finally, the sections were scanned to obtain extra-quality images by PANNORAMIC Pathology Diagnostic Scanners.The tissue of hepatopancreas was sampled as rapidly as possible and immediately transferred to a 1.5 mL EP tube containing fixative, 2.5 % glutaraldehyde in 0.1 M PBS , and held overnight at 4 ◦C in fixative.Subsequently, the sample was secondarily fixed with osmium tetroxide,grow table hydroponic dehydrated with graded ethanol, and then embedded in Spurr’s resins.

Ultrathin sections were cut with a diamond knife and collected on collodioncoated grids by staff in the Equipment Center of the Medical College of Qingdao University.The sections were stained with uranyl acetate and lead citrate and then observed with a JEOL JEM-1200 electron microscope operating at 80–100 kV.After conventional PCR amplification, all secondary PCR products were detected by running agarose gel, and single bands of the 413 bp targeted gene amplicons were detected in the all the samples, as well as in positive control.The sequences of the PCR products were subjected to BLAST analysis.BLAST analyses indicated that all sequences of CMNV RdRp gene from the collected samples showed as high as 98–100 % nucleotide identity with the original CMNV isolate from P.vannamei.The phylogenetic analysis showed that all the CMNV target fragments from three different farm’s isolates were clustered tightly into a branch of known CMNV isolate, which demonstrated higher similarity with genus Alphanodavirus rather than Betanodavirus.Histological examination confirmed that histopathological changes occurred in multiple tissues and organs of diseased shrimp infected with CMNV.Hepatopancreatic tubules underwent necrosis with atrophy and sloughing of tubular epithelium cells.Meanwhile, haemocytic infiltration, karyopyknosis and eosinophilic inclusion bodies were observed between the atrophic hepatopancreas tubules.In addition, extensive karyopyknosis and severe muscular lysis and myonecrosis of muscle fibers that the cell boundaries disappeared were observed in the whitish muscle lesions.What’s more, inspection of Fig.3j indicated massive vacuolation in the cytoplasm of the abdominal nerve.Furthermore, necrosis and exfoliation of intestine epithelial cells were also observed in the diseased shrimp.For further confirmation of CMNV infection in diseased shrimp, ISH was performed by using CMNV-specific RNA probes.

The results showed that blue-violet hybridization signals of CMNV probes were evident in the hepatopancreas, striated muscle, abdominal nerve and intestinal epithelium of diseased shrimp.Massive purple signals of CMNV probes were observed in the tubular epithelium of hepatopancreas, especially the inclusion.Notably, the probes reacted intensely with the karyopyknosis in the necrotic abdominal muscle and vacuolated nerve cells.Meanwhile, purplehybridization signals were also detected in the intestinal epithelial cells.Outbreaks of disease that cause significant morbidity and/or mortalities due to high-density farming and environmental changes in an aquaculture operation are always a major concern.This case report specifically confirmed via PCR, histopathology, ISH, and TEM outbreaks of disease causing by CMNV in local semi-intensive farms in Dongying City and Weifang City, China.Detection results of suspicious pathogens showed that all 28 samples from 4 farms were detected to be CMNV-positive and the viral load of 82 % of samples exceeded 103 copies/μg total tissue RNA.Among them, the frozen bait samples from all of three farms were detected high viral loads, especially the sample of C5-Artemia sp.as high as 2.1 × 108 copies/μg total tissue RNA.Meanwhile, it is worth noting that CMNV-positive was detected in all frozen baits from more other local shrimp farms by investigators.Further, the sequences of PCR amplicons both from the disease shrimp and the Artemia in the phylogenetic tree were highly identical to that of the original CMNV isolate.What’s more, the challenge test results showed that CMNV purified from Artemia can infect healthy P.vannamei and cause a 31.5 % mortality of the infected shrimp within 7 days.Considering that the shrimp post-larva used in the farms were Specific Pathogen Free, and aquaculture water used in diseased farms was underground brine which was free of known pathogens, the CMNV from the frozen bait, Artemia sp.and Acetes sp., was highly suspect to be the origin causative agent of disease on the investigated farms.

These results indicated that most likely CMNV was derived from frozen baits, and then played a significant role in the outbreak of CMNV-infection and high mortality of in indoor farming shrimp that were investigated.In previous reports, at farm ponds level, the cumulative mortality of diseased P.vanname with CMNV-infected could reached up to 80 %.Whereas, the cumulative mortality of CMNV infected shrimp in the challenge test in indoor farming in present study was significantly lower than intensive pond farming.We deduced that the stable and good farming environment might be conducive to reducing the mortality of shrimp infected with CMNV.And this result is also consistent with a recent report in which the result indicated that the lethal capacity of CMNV was related with the farming environment, and the stable farming environment was conducive to reducing the mortality of shrimp caused by CMNV infection.Although EHP was detected in the samples, which may be related to the slow growth of shrimps in local farms , there is no report that EHP can cause obvious shrimp death.Whether the prevalence of EHP might aggravate mortality in shrimp infected with CMNV is unclear.Additionally, in this case, VpAHPND, IHHNV, IMNV, WSSV and SHIV were not the causal agents causing mass mortality of shrimp because none of these five pathogens were detected in all samples.Shrimp sampled from local farms during the outbreak period showed severe clinical and pathological symptoms, typically related to CMNV infections.The moribund shrimp with whitish plaques on the abdominal muscle at the viral infection acute stage was commonly found in the bottom of the pond instead of swimming to the surface or edges.This phenomenon was consistent with that previously observed in P.vannamei infected with CMNV.Although several RNA viruses have been found to cause typical muscle whitening and necrosis of farmed shrimp, it is somewhat different from CMNV infection.For instance, shrimp infected with Infectious Myonecrosis Virus will display evident signs of extensive white necrotic areas in skeletal muscles, especially in the sixth abdominal segment and tail fan, which can become necrotic and reddened in some shrimp.Likewise, Penaeus vannamei nodavirus infected P.vannamei causing muscle necrosis,but the mortality rate was lower than CMNV infection.

What’s more, even though P.vannamei is another species that is susceptible to Macrobrachium rosenbergii nodavirus , this situation usually only occurs under low temperature together with low salinity of aquaculture water leading to significant mortality.The principal target tissues or organs of CMNV are not completely consistent with those of IMNV, MrNV, and PvNV.In this case, the results of histopathology together with ISH showed that atrophic and necrotic tubule epithelium of hepatopancreas with massive purple CMNV positive hybridization signal was observed , which provides reliable evidence that hepatopancreas is the one of the important target organs of CMNV.Meanwhile, CMNV-like particles also were observed in hepatopancreas tissue under the TEM.But, until now, there was no report demonstrated that hepatopancreatic atrophy and necrosis caused by MrNV, PvNV and IMNV infection.The evidences above strongly supported that CMNV was the causative agent of shrimp epidemic in Dongying and Weifang City.CMNV, a novel member of Alphanodavirus, has been found to have a wide susceptible host range, including invertebrates and poikilothermic vertebrates.Initially discovered in P.vannamei , the virus has been reported to naturally infect Mugilogobius abei , Danio rerio , Larimichthys polyactis ,grow table and Apostichopus japonicas , etc.causing damage to multiple tissues and organs.Additionally, previous studies had shown that a variety of cultured crustaceans and invertebrates from shrimp ponds affected by viral covert mortality disease can be infected by CMNV.In this case, through artificial infection experiments, it was confirmed that healthy P.vannamei could be infected with CMNV isolated from Artemia.Although VCMD is a disease that can cause high mortality in farmed shrimp at acute infection stage, and its pathogens also have a wide range of hosts, it is not listed as a notifiable or significant disease of shrimp by OIE at present.VCMD outbreaks have been related to high temperature together with stressful events such as sudden temperature or salinity changes or even high levels of NO2 – – N caused by poor farming environment, and sudden weather changes, even the operation of dividing growing shrimp population to different ponds for reducing density.Meanwhile, CMNV has been shown to be endemic in many countries around the world and caused significant production losses at a national or regional level.This case reported another significant typical VCMD in shrimp culture of China since 2014.

Those results also demonstrated that CMNV is a serious threat to the sustainability of penaeid shrimp and other aquatic organisms’ aquaculture.With the continuous increase in the production of farmed fish, the pollution of the nitrogen-containing excrement of fish in culturing water bodies is getting worse.Aquaponics is a sustainable system that integrates aquaculture and hydroponics technology.By building a synergetic system of fish, bacteria and plants, aquaponic farming pattern converts fish nitrogen excrement into less toxic nitrate nitrogen for plant growth.It is capable of yielding two products in one area, and achieves the dual purpose of agricultural production and wastewater treatment through the conversion and utilization of nitrogen.This pattern has been widely demonstrated to control the nitrogenous dissolved waste as well as purifying water quality , and has great potential for development.Tons of works have delineated that fish intestinal microbiota plays a cardinal role in health maintenance, including metabolic promotion, energy utilization and storage, immune function, endocrine and neuromodulation and enterocyte proliferation.Fish gut microbiota can be generally divided into two types: The native microbiota and the foreign microbiota.The native microbiota, including many probiotics, enhances intestinal absorption and digestion by secreting digestive enzymes and synthesizing vitamins ; meanwhile, it inhibits the proliferation of pathogens and resists the invading pathogens to keep host healthy.In addition, native probiotics have positive effects on improving the structure of gut microflora and establishing the intestinal microbiome balance.Fish farming is threatened by numerous diseases, with the intestine acting as a key transmission route and entrance site for foreign pathogens.Moreover, the composition, structure and function of intestinal microbiota are susceptible to various environmental factors.In specific, under normal circumstances, the foreign microbiota can maintain the dynamic balance of gut microbial community; but when the farming environment deteriorates, these foreign bacteria are likely to have malignant proliferation, resulting in destroying the gut balance and leading to bacterial diseases.Based on these, understanding the composition, modulation and ecological function of intestinal microbiota in various culture environments and culture patterns, is of great significance for aquaculture productivity and sustainability.The diversity and stability of microbiomes in aquaponic systems have received considerable attention recently.In terms of microbiota, researches on aquaponics have mainly focused on rhizosphere microorganisms.There have also been several studies on exploring bacteria living in aquaponics water , pointing out the presence of abundant aquatic probiotics in culture water.However, to our knowledge, scant attention has been paid to the fish intestinal microbiota in aquaponics.Therefore, in the present study, the floating-raft aquaponics were established under controlled laboratory conditions to avoid interfering factors in the open environments, and to further investigate the characteristics of fish intestinal microbiota in aquaponics and traditional farming.Goldfish , as an ornamental variant of crucian carp, is commonly farmed in aquaponic systems.Crucian carp , belonging to the family Cyprinidae, is one of the most widely-farmed freshwater fish in China due to its strong adaptability and high disease resistance.As for the plants, leafy greens are the priority choice for aquaponic systems, accounting for short growth periods and low nutritional requirements.In consideration of these, crucian carp and goldfish were chosen as the culturing fishes, lettuce and water spinach were chosen as hydroponic vegetables, and four different aquaponic systems were established through cross combination.Meanwhile, two corresponding systems without plants were also set up as the control.

A set of variables representing farmers’ goals, the perceived barriers in achieving their goals, problems related to soil condition, and the approach for preventing waterway eutrophication were derived from content analyses, as these questions were open-ended. The responses were analysed with conventional content analysis, in which the coding categories were derived from the data . Content analysis allows the qualitative organisation of large amounts of text into a restricted number of categories , which may then be analysed using quantitative methods. The categories were retrieved iteratively; thus, the coding categories were detailed during the coding process. The codes for each category derived from the content analysis were given as 0/1; 0 indicated that the category was not mentioned in the response, and 1 indicated that it was mentioned. Thus, it was possible to observe more than one category in one response. A more detailed description of the content analysis is provided in Appendix 3. The largest proportion of farms in Eastern Finland were categorised as persistent , followedsa by adaptable , non-resilient ,hydroponic grow system and transformable farms. In Table 2, we summarise the farm profiles according to the resilience typology in terms of the background variables.

The distributions upon which the profiling is based, along with test results for the statistically significant deviations of the distribution amongst the entire survey population, are provided in Appendix 2. The main strategy of the persistent farms can be characterised as satisficing: doing the things that have been done previously without major attempts for development, let alone trying out new things. These farms were small farms typically producing cereals or other crops . Farmers on these farms most often received less than 50% of their total income from farming . However, the farmers were relatively satisfied with the profitability of farming. Environmental aspects did not play a major role in this group, and the persistent farmers were less likely than average to have signed into any of the subsidy schemes observed here. Their farming goals were related to the economy, but also personal goals, such as living on the farm, or a general surrender mentality in which there were no longer any grand goals identified, were relatively common in this group. The farmers of these farms typically had their educational background from vocational schools, and relatively many of these farmers lived alone and did not have children. In sum, the robustness of the persistent farmers arose from them not being dependent on agricultural income, which also meant that they did not have major ambitions for the farm development neither in terms of economy nor the environment.

Adaptable farms aimed at continuous development of the farming business while having a good fit with the existing food regime. The farm size was the largest in this group, as these farms had also previously proceeded on the growth track. Half of the adaptable farms practiced animal husbandry—mostly dairy or cattle; garden crops were also a typical production line. Farming was an important source of income for the adaptable farms, typically constituting 75–100% of their total income. On over half of the farms, the income from farming was more than 15,000 EUR. These farmers perceived the profitability of farming most positively. Farmers in this group were younger than average, and they farmed typically with a spouse and had children.Almost all adaptable farmers identified economic goals, but also social goals such as continuity over generations, sustainability, and contribution to food provision within the society were prevalent. Environmental management played an important role in this group. Larger than average share of farmers managed wetlands and semi-natural habitats on their land. They described the soil condition as good, indicating a tendency for active soil management. These farms had most often opted into the agrienvironmental scheme, which the farmers also perceived as effective. Other subsidy schemes, including the organic scheme, extension support and investment support, were relatively widely utilised by the adaptable farms. The group was by and large characterised by a commitment to farming as a source of livelihood, and a focus on operating by the rules of the regime. To make a living from farming, they had enlarged their farming business in order to keep up with the cost-price squeeze, as well as committed to agri-environmental management on various fronts.

Transformable farms also held a development strategy. However, instead of developing the existing business, they were looking for a new path for their farm-based ventures. Transformable farms were large, and they represented all lines of production, but special crops and animal husbandry other than cattle and dairy were over represented within this group. Farmers in this group were young, had the highest education level of all groups and typically had a spouse but farmed alone. For these farms, farming was either the primary source of income or constituted less than 50% of income. Most transformable farmers evaluated profitability as weak, and they were driven by a search for better profitability. However, such a search had been ongoing in the past as well, as these farms had diversified or applied major changes to farm operations also in the past, indicating the difficulty to find a profitable direction fitting the goals of the farmer. These goals were related not only to the economic performance, as a substantial proportion of transformable farmers also mentioned social goals such as sustainability. Indeed, the environmental aspects played the biggest role in this group, encompassing management of soil condition and nutrients, identification of wetlands and management of semi-natural habitats, important for agricultural biodiversity. Transformable farmers were the most active in utilising the available subsidy measures. Transformable farms encompassed the largest share of farms that also practiced upgrading of products by on-farm processing instead of only producing raw material. In short, transformable farms were trying to do things differently.

The need for transformation stemmed from the efforts to increase the profitability of farming, to make farming a full-time profession, and to reconcile economic aspects with environmental ones. Their perceived barriers were mostly related to markets but also to the farm and its management, entailing issues such as lack of time due to being employed at the farm only part-time or lack of fields. Non-resilient farmers—who form a strikingly high proportion of all farmers—faced a dead-end in terms of agriculture and had the aim of running down the farming business altogether. Non-resilient farmers had a low education level, and they were the oldest in all groups. Even though they were likely to have children, they did not have successors interested in taking over the farm, and thus they aimed at retirement, afforesting, or leasing the fields. The farms were small, and they typically farmed other crops or were in other production. The farmers were mostly part-time farmers, with agriculture constituting less than 50% of their total income, and the farming income was less than 15,000 EUR in 71% of cases. Over half of these farmers had proceeded on a business-as usual track previously, and a substantial proportion had downsized their production in the past. Most non-resilient farms assessed the profitability of farming as weak. Although the majority held economic goals, their frequency was clearly lower than in other groups, and the largest share of farmers in this group identified personal goals such as retirement or maintenance of good health. On the barrier side, social and personal barriers prevailed. Social barriers typically included the lack of a successor or a buyer, and personal barriers included high age and poor health. The soil condition was perceived as weaker in comparison with other groups, and the identified problems in soil condition were often related to the pH status of the fields and lack of nutrients. At the same time,indoor garden even though these farmers felt that the fields suffered from a lack of nutrients, they also mitigated waterway eutrophication by reducing input use.

Non-resilient farmers were most likely to have opted out of the agri-environmental scheme, and those enrolled frequently cited that the scheme did not have any effects whatsoever. These farmers were least likely to be organic farmers and to have received extension support or investment support. With regression analysis, we took a closer look at the predictive power of the explanatory variables in comparison with the general descriptions based on the distributions of the variables. The results of the regression analysis are presented in Table 3, including the statistical significance and odds ratios. When the value of the odds ratio is larger than 1, it implies a positive effect, while a value smaller than 1 implies a negative effect. The model was statistically significant. In the stepwise regression, we included seven explanatory variables that demonstrated the strongest predictive power to classify farms into the resilience groups: farmer age, farmer’s assessment of the farm’s profitability, farm size, education, use of subsidised extension services, adoption and perceived effectiveness of agri-environmental subsidies, and whether the farmer had children. In comparison with the non-resilient farm group, a farm was more likely to end up in the persistent group when the farmer had no children, had a high education level , assessed the profitability of farming as moderate or good instead of weak, and was young. In a similar comparison, the adaptable farm group was characterised by a high education level, positive assessment of the farm’s profitability, large farm size, and young age of the farmer. The farmers on adaptable farms were also likely to have indicated that they had implemented some measures earlier than planned because of the agri-environmental subsidies . Similarly, farmers on transformable farms were young, had a high education level, had used subsidised extension services, and had large farms. Farmers on transformable farms were more likely to have adopted agri-environmental subsidies, which also had an effect on farm management in comparison with the subsidies having no effects. From our data, we have identified four different resilience strategies and differing capacities giving rise to these strategies. The central differences between these strategies lie within their relationship with the contemporary food regime and the related capacities for transformation.

The persistent and adaptable farmers stick to the logic of the dominant regime, while the transformable and non-resilient farmers are looking to shift towards new stability domains outside the dominant regime. This intent is driven by financial concerns: both the transformable and non-resilient farmers are not satisfied with the financial performance of their farms. However, the conclusion drawn differs between these two groups. The non-resilient farms do not have the resources necessary for transformation: they are old, their farms are rather small, they do not have successors, and their education level is low; thus, exiting farming altogether is a consistent intent. The transformable farmers represent the opposite in almost all respects: they are young, their farms are large, they are well-educated and development oriented. The transformable farmers hold latent potential to act as change agents in sustainability transition, but this potential remains so far largely unfulfilled. This is due to the tightness of the contemporary regime: the transformable farmers aim at playing by new rules that do not yet exist. Their operations are not very well aligned with the commercial logic of the dominant regime, yet they have utilised the agricultural policies to the fullest extent; in this sense, they are also confined by the regime. The previous attempts of these farms of doing things differently suggests that finding a profitable direction is a struggle, highlighting the rigidity of the current regime . In this sense, resilience at the farm level is significantly more challenging to achieve by creating entirely new and profitable paths than by adapting to the current macro structure . Adapting to the current macro structure, i.e., the regime logics, is what the persistent and adaptable farmers were doing. Apart from being relatively content with what the regime has to offer in terms of profitability of farming, the strategies of the persistent and adaptable farms were quite different. The persistent strategy was enabled by non-agricultural income. Because of not being dependent on agricultural income, these farmers are relatively robust and possibly able to persist considerable hardships in their operational environment, but for the very same reason, their incentives to continue farming might also be easily lost.