Tag: gardening

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Processing efforts congruent to the cloud infrastructure cannot be provided by the farm server

This documentation directly needs to fit for cross-compliance procedures with public authorities and personal calculations of the farm which requires a standardized format.Furthermore, the billing of tasks shall be processed simultaneously for contractors.To enable task or route planning for the MR or contractor a RRN is required which allows communication to the farmers also without internet.A digitally managed farm becomes resilient if it is characterized by utter independence of external internet connection and power supply.Like this, the FDFS is able “to prevent disasters and crises as well as to anticipate, absorb, accommodate or recover from them in a timely, efficient and sustainable manner”.Nonetheless, all online features and functions are used comprehensively in normal conditions but are replaceable by farm particular systems in case of intermitted power and internet connection.Consequently, a hybrid system was developed, combining cloud-based systems and farm-specific solutions.Likewise, it is for power supply, backed up by an emergency power generator, which is already mandatory for livestock farming.By its installation on the farm, it ensures the decentrality of the data repository.Storing data redundantly on different servers is the main aspect of configuring an FDFS in a resilient manner.Moreover, decentrality results in a higher level of data safety by securing data against external access and loss.All necessary data for the applied precision farming solutions are available in any circumstance.To ensure the connection of sensors, machinery, farm server, and farm management, during an internet outage, an LWN is installed.Less mechanized farms obtain digital farming technology through contractors or machinery rings.When a task is ordered by such latter farmer the contractor’s machinery connects to the LWN of the farm when it is in reach.Data transfer of e.g.prescription maps or documentation of tasks is then enabled.Communication between farm and contractor/machinery ring for task disposition can proceed before task execution with a minimum amount of data within an RRN of long-range like LoRaWAN.The single components of the FDFS in Fig.2 are explained in detail in the following sections.First a farm managed by an FDFS needs a power generator for a redundant power supply.

With technical hardware solutions,ebb and flow table it has to be guaranteed that in case of a power failure no voltage drop occurs.This can be achieved by bridging voltage drops with a UPS implementable for edge computing applications.Its capacities need to be selected according to the data and processing rates of digital sensors and devices running on the farm and the needed degree of resilience of the farm.Farmers have to decide which digital functionalities are fundamental for a similarly effective and sustainable maintained production without the internet thus balancing investment cost and necessary level of resilience.Fig.3 summarizes the general functions which could be covered by the farm server.The first main function of the farm server is to store relevant data for farm management.different data partitions are to mention: Basic data like field boundaries for guidance and calculations, AB-lines for controlled traffic, management zones for variable rate application, flight plans for drones, machinery settings, etc.Open Geodata, provided by public authorities, can be stored in an automated updated version or even several older versions if necessary for specific applications of farmers.Needed versions of satellite images are stored automatically corresponding to actual calculations of e.g.management zones.In level five this function can be conducted via AI, selecting and downloading the currently available satellite images according to the needs of the planned crop rotation.Depending on the cache function of each machine or sensor the server also is supposed to be used as a cache in case machinery or sensor has lost internet connection to send data to the cloud.Data sets of tasks that got interrupted by internet outages are completed in the cloud with cache data from the farm server when an internet connection is reset or over the landline.Some specific data, individual for every farm, might be needed in real-time where no delay by internet connection is acceptable.By receiving and storing these data, redundant to the proprietary cloud service, on the farm server ensures constant access to it.The format of these in real-time needed backup data on the farm server must be read- and processible for offline desktop software.Here ISOXML, JSONLD, and for some applications, Shapeles are the suggested formats.Shapefile and ISOXML are the most common in the actual applications of OEMs.

Data for later use could also be stored in manufacturer proprietary formats and be processed after reconnection to the internet in the proprietary cloud.When purchasing technology, which disadvantageously only offers proprietary formats it is favourable to see if there exists a plugin for converters like ADAPT.Generally, here a clear farm specific definition of necessary data has to be made by farmers because the duration of internet blackouts is never predictable.Among others because of a lack of interacting functionalities between different clouds.Nevertheless, some applications of predictions, machine learning, or simple AI algorithms are possible.In this case, farmers are reliant on the offline performances of their OEMs which evince a great deficit in this sector.Accordingly, secondary processing software needs to be chosen wisely.Focusing on long-term internet interruptions each farm needs to decide about which processed data in which time gap is indispensable.These diagnoses will reveal which proprietary raw data are expected to be made available by manufacturers in open and standardized formats for farmers to be able to use the data in secondary software while the usual FMIS is not available and a desktop version does not exist.Within the suggested FDFS, a desktop version of the FMIS is intended.This is a decisive contribution for maintaining effective, efficient, and sustainable crop production during any internet outage.The FMIS desktop version maintains the necessary functionalities of the specific farm needs.The ideal case of a resilient FMIS would be redundant to the online version which is closely linked to the processing efforts in the former section.Furthermore, interfaces to the LWN connect FMIS and machinery and ensure data transmission and documentation.The FMIS of the presented FDFS provides export functions of common, standardized, and open-source formats which ensures interoperability to secondary software like a GIS for example.When investing in a FMIS farmers need to consider which services their FMIS needs to fulfil in a situation when no internet connection is available.Data communication in between farm server and sensors/ machinery during internet outages is maintained by an LWN.Depending on farm size , topography, data traffic , and required latency of executed applications a corresponding network gets installed.

Farmers who depend on contractors or MRs additionally need to be able to receive and send a radio signal to communicate tasks while internet communication is not available.Otherwise, machinery rings or contractors cannot manage their orders to maintain maximum service capacity.This network does not necessarily need high bandwidth because for management purposes the contractor or machinery ring only needs basic information about the task.The range of this signal is defined by the catchment area of the contractor and MR.To establish and ease the installation of such systems only view standards with low installation costs should be taken into consideration.MRs and contractors, who work for the farmers, need to be able to connect with their machinery and sensors to the LWNs of their customers.When the executing vehicle is in reach of the LWN, the task data can be completely uploaded for task execution.When the job is completed, as-applied and further documentation data are sent to the FMIS through the farm server over the LWN.To complete the independence of the FDFS from external signals, the LWN also covers an alternative for positioning signals.That means all items on a farm, which use position data can navigate or track their position by calculating with signals of the LWN.In case the area covered by the LWN is too small for the farm extension, georeferenced marker points can be distributed on the farm area or mobile antenna stations can be set up.The main use case in the project MRdigital in Germany was in the field of slurry application by a subsidiary company of two machinery rings,flood table which acts as a contractor.A self-propelled slurry applicator is used.Via NIRS technology nutrient contents of the substrate can be measured which enables the system to conduct variable rate application using prescription maps.Such an applicator in addition to digital nutrient measurement is seldom purchased by single farmers and a fortiori, not by small and middle scaled farms.The business model to run such a machine within the organization and the management of machinery rings makes precision farming technology accessible and affordable also for small and middle scaled farms.According to the latter sections, usually, tasks and prescription maps are sent via the internet from the FMIS to the machinery ring or directly to the machine and vice versa.Farmers using an FDFS at their needed level can create and send tasks and prescription maps also during internet connectivity problems.The RRN enables the transmission of simple task data in advance.The prescription map and the as applied documentation data get transferred when the slurry applicator is in reach of the LWN of the farm.The machinery can read data from any kind of FMIS and write it back in the same format.The approach of categorizing farmers’ needs of resilience into five levels tried to meet the majority of conditions farms are exposed to.Nevertheless, many farms might prioritize another sequence of upscaling their level of resilience.For example, a farm with many sensors in the field acquiring low-volume data might prioritize the installation of an LWN before investing in a farm server.The five-level classification is supposed to give orientation for digitizing farms to prepare for crises.Technical setups are very individual and require therefore adapted solutions securing the most important digitized applications from failure.To set up an individual FDFS and choosing the right technical components might over strain the IT skills of most of the farmers, simply because it’s not their profession.Lachia et al.for example, found that the infield use of yield maps is too complex for over 50% of French farmers.

Assembling the components of an FDFS might be even more challenging.But having once set up an FDFS, the maintenance of its components, in addition, can also be expected as very demanding for farmers alone.Consulting and support from independent institutions are needed.A farm server as a central component of the FDFS covers many functionalities from storage to processing and AI to the management of access rights for third parties.This is also supposed to guarantee data ownership and control by farmers on whom has access and uses their data for which purposes.The more extensive these server functionalities get, the harder it can be for farmers to overview and control them.A lot of time might be needed for farmers to incorporate extensive server functionalities.But this should balance itself, the more functionalities the server covers, the more farmers will profit and the more time and money they might be willing to invest and vice versa.Considering that sensors and machinery have cache storage capacities to store raw data when cloud connection is lost, a large on-farm storage server might not be needed, especially if the farm is located in an area with a rather reliable internet supply.But here too, it depends on the applications farmers deploy.Areal imaging for example needs much storage capacities and sometimes low latency which makes it necessary to store and process the images on the farm server to be in time with the following application.The proposed FDFS can be a chance for small and middle scaled farmers in employing digital farming technologies.Only the investment of administrative components, like an FMIS, is sufficient and can, under certain circumstances, be built up modularly according to farmers’ needs and investment possibilities.Machinery and technology are rented or ordered from MRs and contractors.Here we meet the problem of lacking interoperability.When choosing digital components, farmers need to look out for solutions that use international standards on semantics and ontologies, in addition, to open APIs and data formats to ensure interoperability with future or external solutions.Developing such customer- or branch-specific solutions remains the responsibility of established agricultural software developers and solution providers.Solutions including proprietary data formats should be avoided.Also, the MRs and contractors need to take this into account when investing in new technologies.What is not defined in this paper is the detailed organization of data flow at the moment of an interruption of cloud connection.

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Applying AI to the agricultural sector is not a homogenous challenge since the sector varies substantially

All interviews were held through digital meeting platforms and all but two were recorded with the permission of each respondent.The codes mentioned in Table 1 are used throughout this article to refer to the respective interview respondents.Remote sensing technology enables detection and monitoring of physical characteristics of the earth’s surface.Remote sensing data is collected from a distance, commonly from satellites and drones.The three most common properties of remote sensing data are spatial, spectral, and temporal resolutions.Spatial resolution is the pixel size of an image, a property that affects the ability to detect objects through imagery.differently, spectral resolution refers to the spectral sampling intervals size and number which affect the ability of the sensors to detect objects in electromagnetic regions.The temporal resolution regards the frequency of acquired data.The availability and economics of using remote sensing data collection is addressed by Khanal et al., which present remote sensing technology alternatives both open-accessed and for some cost.However, the resolution of the data varies, where the trend is that medium-resolution data is free whereas the prices for high resolution and very high resolution data increase in proportion to their increasing quality.Regarding data resolution, Meier et al. opine that site-specific smart farming depends on high resolution, as detection of anomalies are impossible or insipid with too large pixel sizes.Of course, depending on what kind of analysis the data aims to contribute to, the need for resolution varies.For example, predicting the crop yield within a field can accomplish a high accuracy despite a coarse resolution while detection of plant diseases through hyperspectral imaging requires a detailed resolution.Internet-of-Things is a collective concept for objects with incorporated electronics and connections that enable remote control and information sharing.In agriculture, IoT is mainly used for collecting data through different types of sensors.

By further data analysis,flower pot valuable information can be derived as decision support, e.g.for farmers.Kamienski et al. define four main challenges for IoT development in smart farming.First, the IoT system must have a high level of adaptability.Since the needs of farmers often significantly vary, the IoT system must be customizable to local circumstances but still not increase the required work for the farmer.Secondly, the IoT deployment must be efficient.As Kamienski et al. write, “there is no ‘one size fits all’ in IoT systems”.Thus, each system needs to be configured, the Internet connection and farm infrastructure must be reliable, and the farmer must deploy enough human and economic resources into this process.Furthermore, the scalability is affected by the previous factors but also depends on whether the system, and the models learned, are supposed to work for just one farm or entire agricultural consortiums.Lastly, the complexity of the IoT system can be interpreted as a trade-off between making the middleware broker complex and the software application simple, or the reverse.Another aspect to IoT in smart farming is security.Since the data often is valuable for the farmer and is regarded as a business secret, Kleinschmidt et al.describe the need for end-to-end encrypted communication from the sensor to the application.In practice, this means that the IoT sensor network must have a synced security strategy to the cloud database and the potential fog computing network.By ensuring security, the probability that the farmer trusts the IoT system increases.Still, trust in IoT systems does not just depend on security but also on the precision of the sensors.Without ensuring that there are no systematic measurement errors in the sensors, few farmers would trust the learned model or the real-time data.The potential of smart farming in animal husbandry, such as dairy-, beef- and fur production, is largely constituted by increasing productivity and profitability by streamlining and automating tasks and information.Much research consists of ways to monitor and look after the animals automatically or semi-automated.These articles suggest that devices, both wearable and non-wearable, may be incorporated in the animal stable and that these devices can gather data that can give indicators on the health of the animals.The data that may be gathered through these devices vary, but the wearable devices can measure heat, hormone levels, rut etc.The non-wearable devices more typically are 3D cameras for body condition scoring and infrared imaging, sensors that monitor environment and weather as well as automatic weighing scales and gates.In arable farming, an important feature of smart farming is to be able to calculate the vegetation index of fields or areas to be able to monitor when it is time for harvest and other activities.This may be done by both remote sensing and IoT solutions.

Viljanen et al. train a machine learning model aimed to optimize the “balance between the highest possible yield quantity and an adequately high digestibility for feeding”.By using an inexpensive drone system that can get multi-spectral data from an RGB camera and an infrared camera, traditional physical tools for predicting ley yield can be replaced by smart machine-learned models with higher accuracy.Furthermore, the research of predicting yield and quality of silage can also be accomplished through satellite data, as presented by Griffiths et al..The study shows that it is possible to detect mowing events of grasslands, and therefore characterize the land use intensity by looking at satellite imagery.In terms of yield prediction, Feng et al. stress the importance of incorporating biophysical characteristics of the crop in machine learning algorithms.This means that to learn a model with high precision, it is important to simulate the growing process of the crop to ensure that the model learns the crop characteristics in different stages of the growing process.Furthermore, Matos-Moreira et al.uses manual soil samples to further improve their model.By including manual sampling and analysis with a variety of existing data sources one may learn a model to predict the quality of a crop or the concentration of some matter at a given place and time.Another application of precision farming is to detect sickness or pests among crops.Torai et al. study how diseases can be detected in crops by classifying, or labeling, areas in pictures as “healthy”, “infected”, “diseased” or “aged”.Thereafter, methods such as hyperspectral imaging, Bayesian networks, and an analysis through probabilistic latent semantics are applied to detect the diseases .This study is a good example of a remote sensing technology applied to agriculture which needs a very high resolution of data, preferably on a scale of centimeters.One dilemma when applying artificial intelligence to arable challenges is how to use the different types of available data.Kerkow et al. use fuzzy mathematical modeling to solve this problem.This approach allows for mixing machine learned climate models with wind data and expert knowledge of the landscapes to build precise models .The literature review also brings up some interesting aspects regarding the implementation of AI in agriculture.Medvedev and Molodyakov highlight both theoretical and practical knowledge of smart farming as requirements for successful implementation.Unfortunately, seldom farmers have either the economic resources or the time to attend longer educations within the subject.To meet the lack of technical education within smart farming, Medvedev and Molodyakov propose smaller model-based courses that should cover technical, economic and management aspects to smart farming.

A crucial part of the education is that the courses are on-demand, so that busy farmers can access it whenever it suits them.Both business cases and clear driving forces are named as critical components to spreading the use of smart farming technologies in society.Barriers that hinder the drive towards smart farming are categorized as economic, institutional behavioral, and organizational as well as market.Furthermore, they identify social and moral drivers to play a key role in terms of creating a societal demand for smart farming.Without the support from society at large, innovations will not be adopted by key actors, they conclude.Other research aims to map the barriers to implementing and diffusing smart farming technologies.Kernecker et al., describe that farmers approach smart farming technologies differently given how much smart farming technologies the farmers have already adopted.The so-called adopters perceive the barriers to adopt smart farming technology as high investment costs, a difficulty in interpreting data, a lack of interoperability or precision in devices, that farmers cannot see the added value of the new technology or the relative advantage of the system, as well as a lack of neutral advice from advisors and other actors.The non-adopters also perceive high investment costs and unclear added value as barriers.Additionally, they regard too demanding complexity of use, that the technology is not appropriate for their context or farm size, as well as a lack of access to proof of concept from a neutral point of view, as obstacles.Finally, the literature review highlights the importance of data presentation and visualization, both in arable farming and livestock farming.Beside identifying possible applications of the technology in agriculture, several research groups argue that methods within machine learning and AI require decision support tools that visualize the data in comprehensive ways.One pattern, stated by a farmer respondent,berry pots is that farmers of different agricultural sectors almost always believe that the implementation of smart farming technologies has come further in other sectors than in their own.The agricultural sector that most farmers highlight as currently the most technologically advanced is the milk production.Milk robots were introduced to the commercial market decades ago, and with the milk robots the fodder of an individual cow can be customized, increasing its health status and production capacity.Due to the milk robots, the dairy industry is regarded notably data driven.One important aspect to consider when evaluating the success of the milk robots is the short feedback loop.Since cows are both fed and milked daily, the machines can adjust quickly depending on the latest input.Furthermore, Swedish dairy farmers have a long history of collecting data by being part of the so-called Kokontrollen, a cow data collection application owned by Växa Sverige.Even if Kokontrollen today is web-based, Swedish dairy farmers have been reporting to it for more than 100 years.Previously, all data was collected manually but today almost all data connected to milk production is automatically gathered by the milking robots.Contrasting to milk production, arable farming is diverse with different crops requiring distinct machines and technologies.Hence, a single successful machine is difficult to implement for the entire arable farming sector, making its technological development more complex.

However, it is possible to create effective technology for specific crops.As a rule of thumb, crops with high manual work, such as vegetables, use lots of technology since they operate on small, more controlled areas.In such environments, such as green houses, the feedback loop is faster and there are less uncontrollable factors, such as weather or wild hogs, which makes the application of new technology and AI easier.Of the three agricultural sectors compared in this study, beef production is considered by the respondents as the least technologically developed.Nevertheless, one respondent at a major company believes that meat production will have a central role in the development of the Swedish primary food production.The list of possible innovations includes making the value chain digital by automatically transferring information to the slaughterhouses regarding characteristics of the animals they will receive.By mandatory RFID tags for all cattle, the respondent argues there is an enormous potential, since the development of the animals could be followed in real time throughout the value chain.With such a system, the slaughterhouse could plan far in advance for incoming meat quality and volume.Simultaneously, a grocery store could send data to the farmers regarding the current popularity of different kinds of meat, enabling the farmers to adjust their production to the current consumer behavior.Furthermore, if one could autonomously and automatically weigh the cattle, their growth curves can be predicted which would enable optimization of the timing for sending animals to slaughter.By this optimization, one could avoid having full-grown animals that both drain economical resources and emit environmentally damaging methane gas.Regarding data and the activity of collecting data, the responses from the interviews reflects different realities within the agricultural sector.On the one hand, some respondents say that farmers generally are positive towards gathering data on their farm.On the other hand, some responding farmers state that they collect almost no data on their farms, although they say that they understand that data could add value to them.In-between is a spectrum of attitudes towards data gathering and implementation of technology in the farms.

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Unpredictable climatic conditions could also influence the intensity of fertilizers

The high rates of urbanization and environmental degradation caused in the last decade have negatively impacted on the quality and quantity of food production.Besides the above challenges, there is a problem of nutrient depleted soils and water scarcity across the globe and these are expected to exacerbate in the face of the increasing population especially in urban areas.Traditional farming is generally faced with problems of weather changes, water pollution, soil degradation and soil infertility.Africa alone continues to fight the problem of food insecurity where improved yield and sustainability in the agriculture sector can best be achieved through climate smart agriculture.CSA has been defined as an intervention vital for maintainace of global food security and nutrition through changing and readjusting agricultural practices within the new era of climate change.In order to conserve sustainable crop production systems, there is need to utilize spaces like: non-arable fields that do not support crop cultivation and develop alternative cultivation methods.This justifies the increasing use of various smart agricultural technologies to meet these rising levels of food insecurity.Emami et al.described smart agriculture as the use of technology that has the capability to increase food security if well streamlined to the domestic levels.On other hand, CSA synchronizes actions by researchers, policy maker, private institutions, societies and farmers to promote climate resilient systems, practices and technologies.Arshad Mahmood et al.described hydroponics as an agriculture system for growing crops in water composed of mineral nutrients supported by medium.This system which uses less water as compared to soil farming has successfully been used for cultivation of different vegetables like: lettuce, spinach, cucumbers,hydroponic dutch buckets tomatoes among other crops as these respond well to hydroponics due to low nutrient demands and short growth period.

New drifts in agriculture have shown hydroponics as one of the new innovative soilless farming systems to realize satisfactory outcomes and has the potential to produce more yields in minimal space and promote food security through production of food vertically thus should be considered as a better farming option for East Africa facing a quandary of challenges as earlier discussed.Hydroponic farming has different types which include: Nutrient Film Technique , Wick system, Drip system, Ebb and Flow and Deep water culture.Wick system is the simplest hydroponic method which uses wicks to draw nutrients from the reservoir without use of pumps or timer while NFT hydroponics is a method where shallow channels are used to supply the nutrient solution to the bare plant roots through re-circulation process.DWC is a method of hydroponics in which plant roots are suspended directly into the nutrient rich water solution while drip system uses micro emitters to drip the nutrient and water directly to the plant roots with the help of a pump.Ebb and Flow involves flooding the plant tray with the nutrient solution using a pump that is connected to the solution tank at given time intervals with the use of a timer.The solution is later drained back to the nutrient tank.Adoption of hydroponics in East African countries like: Uganda and Tanzania, where this technology might offer a profitable agri-business and food security solution for urban dwellers by tapping into the growing demand for local produce, is still very low.The potential of hydroponic farming in these developing countries hasn’t yet been fully established.It is likely to be more complicated to provide sufficient food for the fast-growing population using traditional agriculture in future, therefore soil-less cultivation is the right substitute technology to adapt effectively.There has also been a lot of attention given to urban agriculture among researchers, scientists and the general public which calls for more attention into hydroponics as it is considered an urban farming technology.Based on the impasse of challenges presented by conventional farming practices, urbanization and the increasing urban population as well as the ability of hydroponics to tackle these challenges, this study focused on examining the status and perception of soilless farming in Central Uganda and Northern Tanzania as an alternative sustainable cropping system to increasing food security and agdribusiness opportunities around urban and peri-urban areas.

Focus was specifically put on a couple of influential factors majorly socio-economic and agricultural factors surrounding the urban and semi-urban farmers and farms practicing hydroponics in these countries.The study assessed and categorized the benefits, challenges and recommendations for enhancing the implementation of this technology.It focused specifically on vegetable production because research has shown vegetables to be one of the most easy-to-cultivate crops under hydroponics as earlier mentioned.The study was carried out in the months of April-July 2021 in the urban and periurban areas of Meru district located in Northern Tanzania and Wakiso district located in Central Uganda.Tanzania and Uganda are both located in East Africa and experience tropical climate conditions.Tanzania has an estimated population of 58 million while Uganda has approximately 44 million people.Northern Tanzania was selected as study site because it is one of the vegetable growing hot spots in the country and also has a couple of large hydroponic farms in the country while the Central Uganda was selected because it has majority of the urban and periurban farmers engaging in soilless farming.A total of 150 farmers/firms/farms were identified using snowball sampling through farmers groups and recommendations from expert farmers and agricultural bodies.Only 51 participants who practice vegetable production soilless farming technology majorly hydroponics around urban and periurban areas took part in the study.These participants included both farm owners of the hydroponic vegetable farms that as well as managers of firms that produce vegetables using hydroponics for either seed production or vegetables for sale.A pre-tested semi-structured questionnaire using both closed and open-ended questions was designed to capture socio-economic and agricultural factors related to hydroponic farming as well as the benefits and challenges faced by the farmers and farms at large.Socio-economic factors included: age, gender, education level, labor used at the farm, whether the farmer received financial support to implement the technology or not, market for the hydroponic produce and if hydroponics is the main economic activity engaged in by the farmer.

The agricultural factors captured included: vegetables grown, type of hydroponic system used, medium used, size of land used, planters used to grow the crops, kind of fertilizer used, and the environmental setting used to grow the hydroponic crops.Furthermore, it also included questions to capture information on benefits and challenges of using soilless farming as well as the recommendations that can be put in place to enhance the adoption of the technology.Based on the COVID-19 challenges and restrictions, the questionnaire was designed and answered using Google forms and face-face interviews with key informants especially with companies that were engaging in seed production using soilless farming.Due to the limited sample size, data collected was coded and summarized into frequencies using the Statistical Package for Social Sciences version 26.0 and presented using tables and graphs.Previous research has pointed out hydroponic farming to have a number of benefits as compared to other traditional farming system.Hydroponic in general promotes environmentally friendly measures with the ability for improved commercial food production and perform better than traditional open field farms.One of its advantage is the production of good quality crops.Approximately 24% of the farmers recognized this advantage stating that hydroponic vegetables are clean with good color, taste, uniformity in texture and size, and pesticide residue free.Results from a study in Trinidad similarly reported a high willingness to pay greenhouse-“hydroponic tomatoes” compared to “open-field” tomatoes based on being free of pesticides.Hydroponically grown crops have more mineral composition than soil grown plants.About26% of respondents also reported hydroponics to be a CSA system that is not dependant on weather conditions and also environmentally friendly which aspect was also pointed out by Zhigang and Qinchao.Farmers established that hydroponic food production is not dependant on rainfall seasons and neither does existence of drought conditions deter an individual from cultivation hence offers an opportunity for all year crop production.24% of the respondents noted that hydroponics allows production of high harvests within a small space or areas with unfertile soils through vertical farming as compared to the ancient farming system where farmers need huge chunks of fertile land to get big harvests.This makes it a very suitable urban farming system in areas faced with scarcity of arable land.Gholamreza et al.similarly noted that hydroponics gives the opportunity to grow crops in non-arable areas.This farming system can take place in areas with non-fertile soils and can be implemented using vertical farming which increases crop production per unit area through vertical crop cultivation means.Another advantage noted by approximately 20% of the participants was the absence of soil borne pests and diseases with the farming system as compared to soil farming.

The controlled nature of the environment setting for hydroponics, no use of soil for cultivation,bato bucket use of insect traps for both indoor and outdoor systems all play huge roles in dettering pests like white flies hence reducing use of pesticidies.Richard, Charles reported that soilless faming has the benefit of restricted occurrence of pests and diseases.The use of soilless farming gives a unique chance for controlled environment seed production with limited pests and diseases.Approximately 4% reported having control over the environment of the vegetables through monitoring climatic and environmental conditions such as: temperature, Electrical Conductivity , pH and humidity, majorly those who were cultivating under fully automated green houses.With hydroponic farming, there is control over the climatic conditions within the greenhouse environment.Other advantages for hydroponic farming noted by about 4% of the farmers were: no weeding is required, source of income from sale of vegetables and training other farmers, provides supply of fresh vegetables, require little attention during growth and production of surplus food for home consumption.Fig.5att link=”no” categorizes the advantages of hydroponic farming within Tanzania and Uganda.Rice is one of the most important crops for food security and rural livelihoods in many developing countries, especially in the Southeast Asia region.Rice farming plays a crucial role in income generation and ensuring food security for millions of rice farmers in Southeast Asian countries and contributes to food security at the global level through rice export.However, the current rice farming practices heavily rely on synthetic fertilizers and pesticides for higher productivity through improving soil health and preventing damages caused by crop pests and diseases.

Synthetic fertilizers and pesticides are the inputs that farmers cannot self-produce and have to rely on purchase, and the expenses on these inputs usually account for a high proportion of production costs.Therefore, overuse of these inputs lowers rice production efficiency and income from rice farming.Furthermore, overusing chemical inputs poses a major threat to agricultural sustainability , negatively affecting both underground and surface water, and creating eutrophication and losses of biodiversity.Empirical evidence for explaining the overuse or inappropriate application of synthetic fertilizers and pesticides points out several factors such as farmers’ lack of knowledge about optimal levels of input use, significant influence of input suppliers, weak management from authorities, and risk aversion under uncertainties caused by fake products, asymmetric market information of inputs, soil quality, pests and diseases, and climatic variability.For instance, Feder reported that a lack of information about the degree of pest infestation and pesticide’ effectiveness was driving risk-averse farmers to apply more pesticides to reduce the impact of risks.Supporting this finding, Khor et al.indicated that the fear of low-quality fertilizers might be an uncertainty encouraging farmers to apply more fertilizers.Under risk aversion, these uncertainties become significant determinants of fertilizer and pesticide use.Hence, examining the influence of risk attitude in uncertain contexts on the application of fertilizers and pesticides deserves attention.Rural households in developing countries live in a vulnerable context , frequently facing different types of shocks such as weather shocks and crop pests/diseases.These shocks create uncertainties that influence farmers to use more/less inputs.For instance, frequent weather shocks such as floods, landslides, and storms might, on the one hand, discourage rural households from applying an adequate amount of inputs because of their fear of losses.On the other hand, droughts might indirectly and adversely affect systemic insecticides’ performance that leads to an increase in pesticide use.Consequently, farmers might disregard recommended optimal input application rates in the context of uncertainties.Unfortunately, a limited number of studies account for these shocks in examining the relationship between farmers’ risk attitude and input application.

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Studies have found that people readily use stereotypes to fill in details about strangers

Tightness and collectivism are separate constructs, while collectivistic cultures tend to have tighter norms, the correlation is far from perfect.Across 26 nations, collectivism and tightness correlate at r =.49.In other words, 75% of the variation in tightness is separate from collectivism.For example, Brazil and Thailand score high on collectivism but low on tightness , thus the rationale to test the constructs separately.We focus on China’s farming histories as a source of cultural variation.Recent research has found that China has large-scale cultural differences between the north and south that trace back to wheat versus rice farming.Paddy rice farming required twice the labor per hectare as dryland crops like wheat, corn, and millet.To deal with labor demands, rice farmers in southern China formed cooperative labor exchanges.Paddy rice also involved shared irrigation systems and these irrigation systems increased labor demands and forced farmers to coordinate water use and labor for upkeep.There is evidence that the tight inter-reliance in historical rice farming has made parts of southern China more interdependent.In contrast, historically, wheat required less labor and relied mostly on rainfall, which reduced farmers’ need to coordinate.Why would these farming differences be related to pandemic responses?Some researchers have argued that the long-term prevalence of infectious diseases shapes cultural differences.For example, cultures in areas with more disease may be less open to outsiders as a way of defending against people with diseases that are not already present in the community.Because China is such a large country,planting gutter covering climate zones from the tropics to Russia, it has significant variation in climates and disease rates.We tested for the influence of different regions’ experience with diseases by analyzing data on provinces’ SARS cases per population and for the long-run prevalence of flu cases per population.We chose SARS cases because the SARS outbreak captured public attention and thus may have influenced culture.

We chose flu cases as an indicator of more stable differences in respiratory infections across China.Flu cases catch less media attention than SARS, but they affect far more people.If rice farming influences behavior and social coordination, it can potentially explain cultural differences in mask use outside China.By early February, 2020, mask use became widespread in Japan, South Korea, and Vietnam.Newspaper articles in the US and the UK have argued that culture played a role in mask use in East Asia.Thus, it’s possible that countries with histories of rice farming like China, Japan, South Korea, and Vietnam might have been quicker to adopt masks and were thus less affected by COVID-19.However, comparing nations makes it hard to pinpoint the effect of culture.For example, is South Korea’s strong response related to its history of rice farming or the policies Korea put in place after the local MERS outbreak? This is why exploring cultural differences within China is empirically valuable.By looking at differences within a single nation, we can compare people who share the same national government, healthcare system, language family, and other factors.Although this does not eliminate confounding variables, it limits them far more than cross-country comparisons.If rice farming can explain differences within a single country, we may gain insights that could be applied to “messier” cross-country comparisons.The epicenter of the outbreak also provided a rare opportunity to test for regional differences.This is because Wuhan is located in the middle of China.Had the epicenter been in the north or the south, the relationship between proximity and mask use, as well as other cultural factors, would have been harder to gauge.To measure rice farming, we used the percentage of farmland devoted to rice paddies per prefecture.To represent historical rice farming, we used the earliest prefecture-level rice data available from provincial statistical yearbooks.For Study 3, because the search engine data are reported by province, we used provincial rice statistics from the 1996 Statistical Yearbook—the earliest statistical yearbook we could find.To test whether the rice statistics represent historical farming patterns, we compared the statistics to rice data available for a subset of regions from 1914.The 1914 data correlate highly with modern rice statistics.This suggests that the more complete, recent data adequately represent historical rice-farming patterns.Additional analyses found that the results were robust to alternative operationalizations of rice: using provincial rice data instead of prefectural rice data and a simple dichotomous rice-versus-wheat variable.As time progressed, regional governments introduced mask policies.

Mask policies arose first in Wuhan on January 22.Yet even then, many people publicly questioned the effectiveness of masks.At the same time, most reported cases were confined to Wuhan, in Hubei Province.By January 24 , most cities in Hubei were quarantined.Other provincial governments began to introduce their own mask policies afterwards.We measured top-down policies by gathering news reports of official city-level notices requiring residents to wear masks, as well as regulations banning public gatherings and events.Table S8 lists the policy dates and newspaper reports for each observation area.Section S5A describes the data collection in more detail.One plausible alternative explanation to rice theory is that mask shortages determined mask use.Since masks were in short supply, perhaps people in some regions did not wear masks because they could not find one.We investigated this possibility by tracing reports of mask shortages across China.We searched newspaper reports that documented when masks first sold out in different regions.We searched for the word “masks shortage” using Chinese search engines and were able to trace reports and dates for all observation sites.We coded the earliest day a mask shortage was mentioned in the local news.In this approach, we followed the methodology previous researchers used to retrace unfolding events like the 2008 financial crisis and were able to identify that masks were in short supply across all of our observation window.This allows us to explore the possibility of how mask shortages might impact mask use during the early days of the outbreak.As a robustness check, we tested whether rice farming predicted searches for common search terms not related to COVID-19.we ran the same analyses using four of the most popular internet search terms: weather, map, calculator, and translate.This can rule out methodological artifacts.For example, perhaps internet activity was generally higher in rice provinces during this time.If so, rice would predict more searches in general, not just for masks.The results for these common search terms showed no evidence of a spike in rice regions during the initial outbreak.For example, people from rice-farming provinces were no more likely to search for “weather” before the outbreak, in the early days, or in the later days.People from rice provinces did search for “translate” more than people in wheat provinces, but this difference was consistent before, during, and after the emergency declarations.In sum, these results suggest that rice-wheat differences in the early days of the COVID-19 outbreak were not the result of general search differences.

The three complementary studies suggest that rice-wheat differences impacted mask use early in the pandemic.This is partly explained through the mediation of tightness.Rice-farming provinces in China have tighter norms , and people in places with tighter norms wore masks more.Tight norms may have helped pre-modern rice farmers deal with the large labor burdens of rice and coordinate shared irrigation networks.Although this emphasis on social norms emerged for farming, it seems to have helped people in rice regions react faster to the COVID-19 pandemic.However, tight norms explain only a portion of the cultural differences.There are other reasons to think that people from interdependent cultures would wear masks more.For one, research has found that people in interdependent cultures are more focused on risk prevention, whereas people in independent cultures focus more on potential gains and positive outcomes.The data here fits with prior evidence that East Asia is more concerned about virus than people in other parts of the world.A study of the Swine Flu outbreak East Asians reported greater concern about the virus than Westerners, and East Asian air traffic decreased much more severely.A more recent mask observation study found that even with more than 40-days without local COVID- 19, nearly 60% of Shanghai residents still wore masks in public settings , thus pointing to the continual ‘concern’ for others even with no risk of infection.In addition, seeing society through an interdependent lens may make people see the dual purpose of masks: to protect not just the self, but others.Although our studies found little evidence for provincial collectivism measures explaining the differences, it is possible that self-report measures of collectivism do not reflects cultural differences.The fact that cultural differences diminished as the pandemic progressed suggests three theoretical implications: First, cultural differences may help fill in the blanks during ambiguous times.In the early days of the outbreak, the virus was surrounded with unknowns: Is it dangerous? Can it spread from human to human? Is this a true crisis or are people overreacting? It was during this period of uncertainty that cultural differences were the largest.At first, rice areas wore masks.Over time, as cases spread and risk increased, places with looser norms caught up.Culture seemed to help fill in the blanks, before policy, science, and media come into play.There is some evidence that people use stereotypes in the same way.But when people have access to richer, gutter berries individuating information , they rely on stereotypes less or not at all.Second, the results suggest a boundary condition for the influence of culture.Policy efforts can override cultural differences.

While regions responded differently at the beginning of the outbreak, they converged as awareness spread and the government enforced mask policies.This finding can help policymakers understand when cultural differences are likely to influence human behavior and when external circumstances will override initial differences.Third, the data are also consistent with the idea that people do not respond strictly to objective risk factors when they decide how to respond to a public health crisis.For example, death rates were higher for older people, yet they were less likely to wear masks.In addition, COVID-19 cases were less common in places farther from Wuhan.People in wheat farming regions responded to that distance—more people wore masks in places closer to Wuhan, and fewer people wore masks in places farther from Wuhan.Yet people in historically rice-farming regions wore masks at the same rate regardless of distance, local COVID-19 cases, and history of infectious diseases.In sum, ground data from China during the early days of COVID-19 revealed some behaviors that fit with risk calculations and some that did not.It is rational to expect that more people would wear masks in places with more cases, places closer to the epicenter, and places with denser populations.It is also rational to expect that mask use would approach 100% as the outbreak spread and local governments required masks.Yet objective risk factors explained only a portion of human behavior.Non-obvious cultural factors also predicted whether people wore masks, how early people started wearing masks, and whether people searched for masks online.Although COVID-19 was only discovered in 2019, people’s reactions mapped onto their long-run cultural histories.The definition and status of smart farming, sometimes referred to as digital farming , varies from country to country.Smart farming solutions apply information and technologies to increase the economic yield of crop and livestock production, and to optimize farming inputs and processes that extend to the transportation, distribution, and retail phases of the food supply chain.These technologies rely on Big Data Analytics and include cyber systems that afford monitoring, smart predictions, decision support, automated control and future planning.Although there are many definitions for smart farming, the main conceptual elements found in the literature are similar and include combining Big Data Analytics and information communication technologies such as the Internet of Things , and Edge and Cloud computing with farm equipment, GIS technology, robotics, satellite images, unmanned aerial vehicles and algorithms to accomplish farming practices innovatively and efficiently.In addition, smart farms are expected to optimize food production by improving the application of nutrients to the soil, reducing the use of pesticides and water consumption in irrigation.Precision agriculture, precision irrigation and AgInformatic systems are prime examples of current technologies that could integrate ICT for optimizing farm inputs.Yet, the smart farming concept was meant to be more holistic and include frameworks for establishing optimal farm processes, networking of on-farm systems , monitoring the distribution of farm products and marketing food commodities.Finger et al.predicts that smart farming solutions could narrow the productivity gap between developing and industrial countries.

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Its small size and the lack of need for battery make the passive EID well suited for sheep farming

In addition, these included many beneficial bacteria with antimicrobial features, degraders of contaminants and producers of extracellular polymeric substances which are known to improve soil structure and to promote plant growth and drought tolerance. In addition, results are comparable with earlier findings that Firmicutes, including well-known pathogenic Clostridium species, are typical of organically managed plots and are most likely linked to manure fertilization. In general, actinobacterial representatives were more prominent in the organic system for cereal crop rotation and in rotations with manure fertilization. Indeed, high abundance of actinobacteria have been reported in root samples from organic managed soils. Interestingly, our results showed that actinobacterial genus Nocardioides may have benefited from some other organic system specific practice than manure in the cereal rotation. Indeed, actinobacteria have been found to be indicators for no-tilled organic farming systems, and suggested as producers of exopolysaccharides and lipopolysaccharides, and to have relevance in soil aggregate stability in reduced tillage systems. Furthermore, genus Bosea which contains root-nodule endophytic bacteria capable of dinitrogen fixing was specific for the organic cereal rotation system with legumes. There were fewer changes in fungal representatives in the conventional system for the cereal crop rotation between farming systems compared to changes in bacteria. These fungi included soil saprotrophs and mycoparasites which are general opportunists that either benefit from or tolerate synthetic fertilizers or tilling or both. In general, conditions in autumn may favour fast-growing saprotrophic fungi that effectively make use of harvest residues. Conversely, mycelia of AMF are dependent on living plants but as spores AMF may persist in soil even after harvesting. Here, Archaeospora trappei and Archaeospora sp., Glomus mosseae, and Pacispora sp. were indicative mycorrhizal fungi for the cereal crop rotation.

Most of the specific fungi for the organic system for cereal crop rotation were typical of both seasons, indicating certain seasonal stability in the fungal communities in studied arable soils. Furthermore,hydropnic bucket the majority of these specific fungal representatives were the same as the species specific for the manure fertilized plots. Most of them affiliated to ascomycetes and especially to the order Sordariales. Thus, the indicative fungal representatives in both the organic system for cereal crop rotation and manure fertilized plots consisted of functionally a wide mixture of soil and litter organisms, including molds and yeasts acting as saprotrophs, pathogens and predators of other organisms. However, a species of Arthrinium serenense was indicative for both organic rotations but not to manure plots, indicating that it could benefit from some other organic farming practice than manure fertilization. Endophytic genus Arthrinium has been suggested to have various roles in extreme temperature tolerance, production of substances against other fungi and herbivores, as well as acting saprotrophic and pathogenic. Other taxa linked to organic cereal rotation included representatives of Apiosporaceae and Helotiales detected in spring, and the pathogenic Fusarium oxysporum and its antagonist mycotoxin producing Glarea lozoyensis in autumn. These fungi may have the ability to grow quickly and benefit from the second cut of the grass and clover ley which was left on the field as a green manure in the organic system for cereal crop rotation. Precision livestock farming is the application of the precision agriculture concept to livestock farming using a variety of sensors and actuators in order to improve the management capacity for big groups of animals. The PLF is based on real-time data collection and analysis which can be used for animal/flock management .Other innovative tools used for this goal include automats and new technologies . Such innovations become increasingly important as farms grow bigger and single animal monitoring is no longer possible without technological aid .

In intensive farming facilities, the systems achieve this goal through single animal monitoring, environmental microclimate management, feed efficiency rationing, treatment planning and software decision-making aids for the farmer . In the modern farming world of highly industrialized systems with extremely low ratio of farmers to farm animals, it provides a crucial component in the ability of the stock person to keep track of its animals . The levels of monitoring provided by electronic tags and animal-based sensors for a single animal improve the ability of stock persons to manage each animal individually and respond to health problems or welfare issues faster than manual detection . The efficiency granted by the application of PLF and other technologies is also important to the reduction of farm waste and the reduction of the number of animals needed in order to produce the same amount of product increasing farm environmental and economical sustainability. In the farming of ruminants, PLF application has seen the highest implementation in the dairy cow sector as farm intensification took place in the developed world. This sector also enjoys a high level of competition between PLF developers which tends to improve PLF products as well as technical services. Dairy cow farmers nowadays are aware of the variety of management tools at their disposal and of the need to understand and implement those products in an increasingly competitive market . Other ruminants, especially ones kept in the pasture, are less likely to benefit from such systems. An extensive pasture environment is more difficult to control in comparison to a closed barn, especially in regard to infrastructures and communication options . Extensive farmers prioritize methods of grazing with low financial investment and relative simplicity of management which provide a level of economic resilience to market fluctuations. Therefore, adding PLF systems would inevitably increase production costs and would add another layer of technological complexity to farm management . Nevertheless, technological solutions are being gradually incorporated in extensive pasture farming of cattle and small ruminants . A particular sector of extensive sheep farming is the dairy sheep farming around the Mediterranean which has unique characteristics tied to its climate and cultural conditions. This led to the development of a variety of local breeds specialized in milk, with yields more than double the world average.

The production supports a diverse consumption market of sheep dairy products with global exports and known trademarks such as the Greek ‘Feta’ or the Italian ‘Pecorino’. The market and farming systems of the area were recently described in a review by Pulina et al. which highlights the global relevance of the sector: around the Mediterranean and Black Sea regions are concentrated roughly 27% of the world milk yielding ewes, providing more than 40% of total sheep milk production. Almost half of it is concentrated in 4 south European countries – France, Italy, Greece and Spain with over 15 million ha of land used for grazing . From the farmer’s perspective, Mediterranean flocks are usually small to medium size with high levels of specialization for milk yield where meat production is usually limited to light lamb consumed during traditional events. Fibre production for wool is negligible and the income ratio of the production is usually 38:62 of meat: milk clearly favouring milk production . The FIGS production includes modern characteristics, with breed selection programmes, commercial processing and Protected Designation of Origin nominations for their traditional cheese products . Farming systems include traditional extensive farms based on pasture as well as intensive systems that take advantage of modern technologies and precise nutrition management. While the intensive systems are favoured for their higher yield, extensive systems are not neglected due to their lower maintenance costs and better resilience to milk price fluctuations. While the integration of PLF and new technologies is accruing, it is associated with intensive farms which adopt systems similar to ones practiced for dairy cows . Extensive dairy sheep farming is a unique farming system, where animals are grazed outdoors, while maintaining contact with the farmer during daily milking for 120–240 days a year. This intensive handling process has no equivalents in the meat and wool production process where animals are handled only in specific occasions. This contact can be used for data collection by dedicated technological solutions, data that could aid in the feeding,stackable planters breeding and management of the flock. The current paper aims to present the technologies currently developed for extensive sheep farming and their potential to be incorporated in a small to medium scale dairy specialized farming system typical to the Mediterranean area. Also discussed in this paper are the current trends of PLF implementation as well as sheep farmers’ attitudes towards innovation, technology and systematic management due to their inherent influence on the adoption of any new technology in the field.A literature review was performed in order to evaluate the current state of PLF and new technologies that can be adapted to the Mediterranean extensive dairy sheep farming sector.

Literature was reviewed in order to identify PLF systems, innovative technologies and automats available and under development. The search was carried out in a manner similar to Lovarelli et al. on Web of Science®, Google Scholar® and Scopus® databases, focusing on studies carried out in the last 20 years . The following keywords were matched for the search: ‘PLF’, ‘sheep’ and ‘dairy sheep’, ‘PLF’, ‘extensive farming’. As the search yielded various PLF systems, each one received a further search, for example, ‘RFID’, ‘sheep’ and ‘extensive’ or ‘WOW’, ‘sheep’ and ‘management’. This process was performed for each one of the described technologies. Articles regarding precision medicine, precision diagnosis, as well as advanced bacteriological and parasitological diagnostics were excluded from the evaluation process. Following this selection process, a total of 154 articles were included into the initial database. A panel of three independent evaluators were given 52, 51 and 51 of the articles respectively. The lists of articles were then exchanged until each panellist covered all the 154 initial articles, and each article had three independent evaluations. Technologies and PLF systems were therefore grouped and described according to the collected conclusions of the three panellists. The further search focused on technology adoption by farmers. As the number of articles for exclusively sheep farming is limited, other farming sectors were also considered. When Mediterranean references were not available, articles regarding the applicability of systems in other places were discussed. This included EU member states, as well as very different farming systems . Consideration regarding PLF and new technologies future role included financial, cultural and environmental trends. Market prices of products and commercial data were collected from official sites of the producers, distributors, online stores and local selling agents . Financial information was obtained from consultant websites dedicated to farmer finances while Common Agricultural Policy and payment schemes were obtained from extension services of farmer’s co-operative associations. The CAP payments are a result of a common policy for all EU member states, funded by EU’s budget while the management is mostly delegated to local authorities .Electronic identification systems are a key component in PLF setting of farms and the only technology currently mandatory under EU laws . Radio-frequency identification systems allow each animal to be identified independently and the data to be stored and used for various decision-making processes. It is also a key component in animal identification for other PLF systems such as the weighting scale or AD. Passive EID tags are based on the storage of simple information code and a copper coil which briefly charges the transmitter through the energy passed from an active reader .Under the EU legislation, the use of EIDs is obligatory for all sheep and goat farmers, and currently represents an opportunity for introduction of PLF system into extensive management systems. The RFID operates on different radio frequency levels which determine their transition distance and ability to pass materials : low frequency , high frequency and ultra-high frequency . In farming , three significant differences can be distinguished between them: while the HF and UHF are working in the upper level, allowing anti-collision, longer distance, less noise and stable connection they hindered by materials . The LF is less stable but allows better passage through obstacles with the disadvantage for the need of larger antenna. In some cases, farm metal can act as an antenna itself and have multiple reading . It is the most common method of application. Its ease of use and application makes the method very appealing to farmers.