gardening – Page 2 – Nature Ecrogrow

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

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

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

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

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

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

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

Mountain farming faces several natural and technological limitations

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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