Month: August 2022

While these changes have a positive effect on the ability of lower caste groups to attain resources and engage in dairy farming , it also shows that 48% of the HHs participating in this study had no livestock, and 6.8% kept livestock only temporarily in contrast to the past.This also suggests that those who cannot afford intensive livestock production tend to reduce their livestock rearing or to rear small ruminants as needed, thus indicating marginalization.In view of the above, it is necessary to re-assess current approaches in ongoing WDPs as intensification and specialization, do not necessarily result in higher economic performance, especially in biophysically constrained environments such as dryland areas.Our reason for emphasizing the biophysical aspect is that, despite the better standards of socio-economic and infrastructural conditions in Telangana , the lower economic performance in farming is still observed and across all farming systems.We therefore suggest considering alternative development strategies for HHs, such as “area-wide integration”, feed self-sufficiency, or farm diversification to triggering better economic results or enhance the viability of farms in the long term , particularly in environmentally constrained regions.Further, to manage the dynamics of intensification and specialization in farming systems , the institutional capacity-building at the village level in WPDs should be strengthened with new information and approaches.This is well demonstrated by some civil society organizations, using community engagement approaches and tools.Such approaches, combined with science-based evaluations of ongoing programs,flood tray could help avoid the implementation of conflictive technological development and create knowledge about complex social-ecological processes.

This approach could also facilitate an interactive learning space and promote local innovations by tapping local or traditional knowledge systems to improve the management of dryland environments.In all, we urge the need for interdisciplinary research to assess the relative feasibility of varied farming systems in dryland conditions, the socio-economic impact of agricultural intensification in dryland ecosystems e.g., indebtedness and access to credit, HH dietary diversity or gender implications.Also, we encourage the implementation of mechanisms that can facilitate continuous research on farming systems development and their economic and environmental performance.This will help to better anticipate farming systems trajectories and the potential effects of development strategies, also those within the WDP operational framework.Worldwide, agriculture is facing a double challenge of increasing productivity and developing more sustainable ways of food production.Small-scale farming practiced on relatively small plots of land is the most dominant form of agriculture, constituting more than 70% of the global food production entities.Family farmers with small landholdings represent about 80% of the world’s farms and account for 85% of global population involved in agriculture , mostly in low and middle-income countries , with strong strain on natural resources and pressing concern for food security ; and addressing multiple goals and targets contributing to achieve the Sustainable Development Goals.Although widely used, a unique and unambiguous definition of smallholder farming still remains to be established.It currently relies on several criteria, mostly related to land endowment , labor productivity and income.The definition of smallholding is however context-dependent and can vary according to socio-economic, technological and agroecological realities.

SHF systems are highly diverse in terms of climatic, ecological and socioeconomic conditions as well as in their structure and functioning.Still, these agroecosystems share certain properties like high levels of biodiversity and complex landscape composition , key role of family-managed farms in supporting local livelihoods , management methods tightly related to rich local knowledge system or shared cultural values in common social organization and strong adaptability to changes, sometimes in high risk environments.These agrosystems are also a leading representation of human-nature interactions and feed backs, encompassing material and non-material benefits for humans as well as threats or unfavorable outputs.As for other ecosystems, long-standing interactions within SHF and their ecological functions provide direct and indirect fundamental benefits to humans, through supporting, cultural, provisioning and regulating ecosystem services , 2005.Because of the strong interconnected natural and agricultural features in SHF, unsustainable practices may undermine ES on which smallholders depend to meet urgent needs in contexts of great vulnerability and weak institutional support.Food production on SHF is strongly linked to biodiversity-derived services as increasing the levels of artificial inputs is not economically viable for resource-constrained households.Therefore, options to maintain or improve production are rather linked with improvement of the amount and integrity of ecosystem regulation and supporting services , 2013.Food production, especially in SHF, depends on a wide range of ecosystem functions including nutrient and water cycling, pollination, competitive interactions, and matter decomposition.These functions are fulfilled by several agrobiodiversity components, particularly arthropods.

To date, research on arthropodrelated ES has mainly focused on well-known functions and performed by charismatic or iconic groups such as butterflies, hymenoptera or beetles , even though a large part of global crop production depends on pollination from bees and wild pollinators.Pollination also contributes to economic welfare and to rich and meaningful cultural and spiritual life for a large population.Along with pollination, biological control is one of the most studied services as it implies high economic impacts for agriculture because parasitoids and predatory arthropods contribute to controlling pest insects in crops.In contrast to ES, ecosystem disservices are defined as ecological elements, functions and processes affecting negatively human well-being, directly , by intermediate of negative impacts on ES or by reinforcing other EDS.EDS scope on ecological phenomenon linked to negative outcomes affecting human well-being, which must be differenciated from the associated detriments or costs resulting from human actions on ecosystems.In agricultural systems, EDS affect functions and productivity, leading to important crop losses.These disservices such as herbivory or competition for resources have also been extensively studied, establishing a dominant viewpoint where insects are predominantly perceived as crops pest and harmful to anthropogenic environments.Nevertheless, as stakeholders’ actions may be largely driven by greater perception and willingness to reduce EDS , arthropod management for either mitigating EDS or enhancing ES can also be a powerful driver for transition towards sustainable agriculture in smallholder systems.In particular, promising results on agroecosystem management towards more sustainable agriculture have been reported when including ES-EDS synergies and trade-offs.To date published evidence on the relationships between arthropod related ES and the sustainability of agricultural practices has been largely based in research from high-income countries and temperate regions.

Moreover, a combined analysis of services and disservices of arthropods in SHF systems has still to be performed for balancing positive and negative impacts of nature on human well-being and for reframing entomological research to achieve the SDGs.To address this issue, we performed a literature review capturing research trends in insect-related ES and EDS in SHF, detecting knowledge gaps and exploring to what extent these studies are conducted within a transdisciplinary framework.In particular, we were interested in research practices in SHF considering ES and EDS in a multidimensional view of agroecosystems and bringing together diverse knowledge systems, especially between academic and farmer communities.We conducted a systematic multilingual review of the scientific literature in peer-reviewed journal articles published between January 2015 and January 2021.We followed the systematic literature review approach and the six steps protocol commonly used for scientific review.Detailed steps of the process are described in Appendix A.We first determined the research scope with the PICOC framework.We identified concept groups for keywords from the terminology identified in PICOC and then ran a ‘naïve search’ for identifying search terms through an automated approach using the litsearchr R package version 1.0.0.Then identified terms in the three languages were searched in different databases covering a broad range of academic contexts: Web of Science , Scopus , BASE , and Scielo.The search string was a compilation of keywords of four main domains: Arthropods, Agriculture, Ecosystem services and disservices,ebb and flow tray and Smallholder farming.Keywords were searched in aggregated quests, progressively filtering articles, thereby giving us an idea of the shared publications of each sub-theme in the overall literature on arthropods.Overall, we retrieved 454,703 records on arthropods, of which 40,720 were related to agriculture.Among them 14,967 articles were related to ES or EDS, of which 1564 concerned SHF.As diversified international databases and collection of published scientific research help cover citations more widely , especially for countries in L&MIC, we included bibliographic resources from other scientific search engines, scientific libraries and scholarly journals platforms as Dialnet, PKP Index and AJOL , using the four main keywords groups repeatedly in the search process.Finally, we conducted a complementary approach of citation tracking by backward snowballing using articles’ reference lists.We retrieved 57 additional references, leading to a total of 1621 articles.All references were compiled into a unique bibliographic database organized and arrayed to eliminate duplicates and misreferenced entries using the revtools v.0.4.1 and synthesisr v.0.3.0 R packages.Article titles and abstracts in the resulting database were subsequently screened to complete inclusion-exclusion procedure according to predefined criteria.

We excluded publications whose focus was not relevant to SHF systems or for which insect sampling was not done under real world conditions.This also implied excluding studies about intensive and high-input farming systems and those located in HICs.Moreover, we excluded papers in which insects were not associated to any disservices or EDS.After this selection process, our database included 172 publications.These were selected for full screening and qualitative assessment, after which 122 publications were kept.The remaining 42 articles were excluded in the last full-text reading step when arthropods were not explicitly mentioned or ES and EDS were not clearly addressed.For the final data extraction step, we registered in separate subset datasets all information related to ecosystem services , entomofauna and farmer knowledge and perceptions.Besides bibliographic default metadata, we registered data about country, income level and study system as well as scientific methodology variables.We defined four main thematic to analyze the articles listed in the final database and extracted information on arthropods, their services and disservices, farmers’ knowledge and actions related to arthropod management; the transdisciplinarity approach of the research.First, we examined the taxonomy of arthropod communities and at which spatial scale they were studied.This issue is important when assessing arthropod-related ES and EDS as understanding arthropod dynamics typically requires studies at the landscape scale.For this, we reported which habitats were included in the study.Second, we used the four Millennium Ecosystem Assessment’s EDS were visualized through a network analysis using the R bipartite package.In addition, we extracted diversity data of arthropod taxa related to ES or EDS.Third, we gathered information on the type of farmers’ knowledge and associated management practices regarding arthropods in their farming systems.We also recorded all actions mentioned in the studies for subsequent classification of values based on arthropod management strategies  and whether chemical pesticides were used.Fourth, we analyzed to what extent the research works had been developed through a transdisciplinary approach.Transdisciplinarity addresses relations between science and society, making transformations from science building process and involving stakeholders since the first stages of research process to better target problems.To assess whether research processes encompassed knowledge co-construction and sharing, we set a farmers’ participation index adapted from the typology proposed by Pretty and Brandt et al..The five levels of the FPI reflect the degree of involvement of farmers in research process, from an absence of farmers or no implicit participation to a shared and coordinated implication of farmers in research.In addition, we identified the person involved in arthropod identification.All statistical analyses and graphs were performed using R 4.0.4.The 122 selected studies were conducted predominantly in SubSaharan Africa , Latin America & Caribbean and East Asia & Pacific.Overall, 44% of the studies were conducted at a regional scale, 39.0% focused on local scale and 15.0% covered national or transnational scales.In total, 79.5% of the publications were English-language performed, followed by Spanish or bilingual version English/Spanish and French.Research disciplines concerned mainly “Agriculture and Agronomy” , Ecology-Biologyand Entomology , with a low occurrence of studies belonging to social sciences, economics or multidisciplinary approaches.The majority of publications focused either on crop fields , agroforests or crop storages , encompassing 68 different crops.In most cases , those systems were polycultural with monoculture and mixed systems representing 22.2% and 17.1% of the studies, respectively.Most works studied insect-plant relationship only at the plot-level and only 29.8% included the surrounding habitats.Because several services could be analyzed in a single study, the total number of studied ES and EDS was higher than the total number of studies.Most studies focused on regulating ES and EDS.Only 6.86% of services referred to cultural services, and even fewer to provisioning and supporting services.Overall, 16 main categories of ES and EDS were covered.

Access to infrastructure is considered to influence the feasibility and efficacy of aid distribution programs in response to disasters and used to represent physical capital.Given that better access to power services may reduce the impacts of winter storms by providing alternative or additional assistance, access to facilities was used to represent physical capital.GIS data on power plants and facilities were obtained from the U.S.Environmental Protection Agency’s Facility Registry Service and Iowa Facility Explorer.The interviewed farmers also reported that a major winter storm loss on farms was from animal death caused by inadequate feed.Thus, feed supply was also considered as a physical capital indicator and represented by the 2012 feed expenditure data collected from USDA QuickStats.Human capital.Labor is considered to make a positive impact on vulnerability reduction because more family members can increase work efficiency during both events and subsequent recovery.This study used household size and labor expense as human capital indicators to represent the availability of labor engaged in adaptation.Education level, which is considered to increase the adaptive capacity by enhancing access to information , was also included to estimate human capital.The more skills and knowledge acquired, the more capability households have for emergency planning, recovery, and decision-making.Data on household size, labor expense, and education level were collected from the US Census Bureau.Social capital.Social organizations can improve adaptive capacity by enhancing social networking.Households with a membership to farm-related organizations are more likely to receive support or benefit from the professionals.To obtain information on membership with the agricultural organizations, a request was submitted to the contact on the Practical Farmer of Iowa website.Interview results also reveal that the reduction of storm losses can be attributed to the registration of insurance packages and government programs.More investment in government programs could provide more support during the storm recovery process.

The government program expense used in this study was retrieved from USDA QuickStats.Overall,mobile vertical farm a total of 12 adaptivity variables, 2 sensitivity variables, and 2 exposure variables were selected for the assessment of rural winter storm vulnerability.Socioeconomic statistics and spatial information were all aggregated to the census county level and standardized to Z-scores in SPSS before further analysis.There are 29 out of 60 significantly correlated pairs with a p-value of less than 0.050, indicating strong interrelationships between indicators.Hence, these indicators are considered suitable for factor analysis to extract principal components accounted for by the variable correlations.The correlation coefficients range from − 0.459 for farm income and natural shelter to 0.788 for farm income and labor expense.Counties planting more trees appear to receive lower income.Labor can increase farming productivity and, at the same time, require more investment, leading to the strongly positive relationship between farm income and labor expense.There is also a strong correlation between membership counts and education, indicating that counties with higher education levels are more likely to subscribe to farming associations.Among the selected 12 variables, poverty, energy, internet operations, and household size yielded low community values , suggesting that they would be weakly reflected via the extracted factors and thus be removed from factor analysis.Finally, with the remaining 8 variables, factor analysis extracted the first 3 factors that could yield a total of 85.124% of total variance explained , with an acceptable KMO value of 0.627.The Bartlett’s Test was statistically significant, indicating the high independency among the 8 variables.The loadings matrix in Table 5 shows the correlations of each variable with the three extracted components.Those with loadings greater than 0.800 are considered as salient indicators representing the three underlying dimensions of adaptive capacity determinants.The first factor is interpreted as farming economic status based on its salient indicators of labor expense, farming facilities, and farm income.This factor is considered to project adaptive capacity more accurately as it accounts for the largest total variance of the input variables.Economic conditions may be the most important determinant of adaptive capacity, probably because economic resources can facilitate technology implementation, ensure training opportunities, and lead to political influence.The second factor has high loadings on natural shelter and government programs, hence it is explained as environmental institutional capital.This factor may suggest a strong correlation between institutional efforts and the enhancement of environmental services.

For example, through general or continuous funding, the state of Iowa has a variety of conservation programs aimed to provide cost-sharing for tree planting on a highly erodible row crop and pasture land , potentially increasing farmers’ adaptive capacity to winter storms.The third component is highly correlated with education and organization membership.These indicators representing human capital and social capital are considered to affect innovative performance.Therefore, innovative capital is reasoned as the theme for the third component of adaptive capacity.The overall exposure rates are high in Northwest and Southeast Iowa due to high event frequency.This is consistent with the long history of severe winter storms and blizzards recorded for these regions.In contrast, eastern Iowa shows the lowest exposure scores.Sensitivity indicator scores were calculated by summing the standardized variable scores for animal sale and building age.As shown in Fig.4, counties peripheral to central Iowa tend to be more sensitive due to a high percentage of the total sale from animal commodities.From East to Central Iowa, the counties are light-colored, indicating low rates for building age and animal sale.This contributes to the notably least overall sensitivity for Polk County and its surrounding counties.Several counties score high in animal sale and/or building age, leading to their high overall sensitivity scores.Fig.5 shows the overall adaptive capacity and individual factor scores.Figure 5a shows that the adaptive capacity is low in most northwestern counties in Iowa and high in central Iowa and northeastern margins.It is noted from Fig.5b that counties in northern Iowa have higher rates for farming economic status as they have higher labor expense, farm-related income, and farming facilities than counties in the southernmost part of Iowa.Sioux appears to have the best farming economic status, as opposed to the metropolitan regions where farming-related investments are low.Fig.5c shows that the northwestern quarter of Iowa is low in environmental institutional capital, with limited natural shelter and low expense on government programs.This may be because the long-standing large tracts of wetlands concentrated in the northwest and north-central parts of Iowa have provided rich farmland for growing intensive crops.The increase of mono-cultures and the decrease in livestock pastures in the northwest could lead to the destruction of windbreaks.The patchwork of small, diversified fields that once were common remains in southeastern Iowa.

In northeastern Iowa, the rugged landscape with more wooded areas may have prevented farms from expanding to large industrialized operations, resulting in high index scores for environmental institutional capital.Fig.5d shows a concentration of innovative capital in the metropolitan areas of central Iowa and cold spots in northwestern and southeastern Iowa.Fig.6 illustrates the overall vulnerability for all Iowa counties calculated using the overall exposure, sensitivity, and adaptive capacity scores.In general, southern counties such as Adams and Union are remarkably vulnerable to winter storms, perhaps because much of their land areas in southern Iowa is used for perennial pastures , increasing their sensitivity.Highly vulnerable counties are also clustered in the Northwest where winter storm events are more frequent and in the Southeast where winter temperature deviation is higher, both reflecting high exposure.The vulnerability is low in central Iowa due to low sensitivity from East to Central Iowa, in particular in Polk and its adjacent metropolitan areas.Counties with low vulnerability are also found in northeastern Iowa where adaptive capacity is higher.Among different disaster types, winter storms receive limited attention, while they cause non-negligible costs.In Iowa, there appears a generally increasing trend in experiencing winter storm events, indicated by more above-average event occurrences in the recent past.Evaluating the vulnerability of farming communities to winter storms in Iowa has implications for identifying counties’ agricultural production prone to winter storms and thus reducing farm loss during winter storms by managing the vulnerability components, namely, exposure, sensitivity, and adaptive capacity.Exposure can be influenced by the increased population and assets at risk as a result of population growth in locations at risk from natural hazards , and storm impacts are likely to be worse in more populous areas than others.However, Polk County – the most populous county in Iowa – rated the least vulnerable to winter storms,vertical farming racks whilst it has relatively high exposure.Its low score in vulnerability may be due to their industry-oriented development that is more resistant to winter storms than farming activities.This indicates the severity of weather events is not necessarily consistent with the population pattern alone as it may vary depending on the specific disasters or economic structure.To explore the issue further, the difference between vulnerability level and factual on-farm loss in 2012 per county was calculated and illustrated in Fig.8.After scaling to the range of 0–1, the overall difference ranged from 0.009 for Johnson County to 0.88 for Van Buren County.

Counties graphed in the left half of Fig.8 show almost identical distributions of farm loss and vulnerability.This implies the selected indicators for winter storm vulnerability in the current study may be used to effectively evaluate the general farm losses for these counties for a given year.It is found the metropolitan county of Story has non-negligible farm loss and underpredicted vulnerability.This suggests the limitation in the current model that is unable to capture all critical factors to determine the area’s general farm loss.For example, farming intensity may scale the loss but is not considered in the model.Agricultural production characteristics such as the quantity of products vulnerable to other storm events as well as meteorological variability such as winter storm occurrence may also contribute to the discrepancy between empirical farm losses and predictions.To account for all counties’ general loss characteristics determined by factors not included in the current winter storm vulnerability model, the 2002-2017-census-year average farm loss was calculated.Several counties in the left half of Fig.8 show small differences between farm loss in 2012 and average farm loss, indicating these counties have relatively stable farm loss patterns and the current model can be used to evaluate their long-term general farm losses.On the other hand, counties displayed on the right half of Fig.8 reveal large differences between the predicted vulnerability and farm loss in 2012.This may be due to meteorological variability and generally low farming loss.For example, Hamilton County has a high difference value between the predicted vulnerability and farm loss in 2012 but a low difference between the predicted vulnerability and average farm loss, suggesting the model may not be suitable to predict farm loss for certain years due to variable winter storm occurrence.Van Buren County shows a high difference value between the predicted vulnerability and farm loss in 2012.Yet its average farm loss and farm loss in 2012 are equally low perhaps due to its low farming intensity resulting in consistently low farm losses.Key ways to reinforce adaptive capacity and reduce sensitivity include providing incentives for diversification and tree planting programs as well as enhancing innovative capital, facility investments, and subsidies.The high winter storm vulnerability may be reduced in northwestern and southeastern Iowa, where farms rely heavily on pastures and receive more winter extremes and anomalies through increasing environmental institutional capital, such as engaging more nursery professionals in vulnerable areas to assist livestock farmers who want to plant trees and shrubs.Innovative livelihood strategies such as diversifying income into other sources may be helpful for economic development in the Southeast.In southern Iowa with poor farming economic status, subsidies and facilities can also play an important role in offsetting the negative impacts of financial problems.Previous studies have shown that the spatial resolution of census administrative boundaries is the principal factor affecting map accuracy.Indicators presented at an aggregated level may be unclear or distorted.As a result, the use of census data at the county level which includes metropolitan areas can affect vulnerability patterns for farming communities as it fails to distinguish urban-rural contrast in terms of farming characteristics.To address the issue, the three vulnerability components scores for rural Iowa were also calculated and mapped exclusively for rural counties.By comparing it with Figs.3–5 that include non-rural counties, it is observed that the exposure pattern remains the same and few significant pattern changes are found for sensitivity.

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

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

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

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

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

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