Assessing the Effectiveness of Java Developed ...

Download PDF
4 downloads 0 Views 770KB Size Report
Comment
Java supported mobile applications (M-apps) in meeting the ... Developed Knowledge Management Systems (JDKMS) and M- ..... Applications like M-PESA in.
International Journal of Trend in Research and Development, Volume 4(1), ISSN: 2394-9333 www.ijtrd.com

Assessing the Effectiveness of Java Developed Knowledge Management Systems and Java Supported Mobile Applications as Tools to Enhance Sustainable Agricultural Production: A Case Study of Zimbabwe 1

Paul Mupfiga, 2Newman Ishe Chinofunga and 3Natsai Chapwanya, 1 Lecturer, 2,3M.Sc Student, 1,2,3 Department of Computer Science & Information Systems, Midlands State University, Gweru, Zimbabwe Abstract-- This paper assessed the effectiveness of java Developed Knowledge Management Systems (JDKMS) and Java supported mobile applications (M-apps) in meeting the objectives of ZimAsset i.e. promoting Food Security and Nutrition through enhancing and sustaining agricultural production. This paper will evaluate whether the use of Java Developed Knowledge Management Systems (JDKMS) and Mapps can result in improved food security, economic growth, employment and poverty eradication .Finally, the paper will assess the impact of these on the environment and agricultural sustainability. Keywords-- Java, android, mobile apps, ZimAsset, sustainable agricultural production, Food Security and Nutrition I.

INTRODUCTION

According to the United Nations (Zimbabwe Census UNDP HDR 2013), The Zimbabwean economy is reliant on agricultural with a contribution of 17% to GDP (Zimbabwe Medium Term-Plan 2011-2015). Climate change has reduced productivity in the past decade (undp.org/) resulting in increased food and nutrition insecurity. According to the United Nations Development program (www.wfp.org/countries/zimbabwe) Zimbabwe is a lowincome, food deficit country, ranked at 156 out of 187 on the 2014 UNDP Human Development Index. Currently, an estimate of 72 percent of the population is living below the national poverty datum line (living on less than USD 1.25 per day). Thirty percent of the rural poor are considered to be ‗food poor‘, or ‗extremely poor‘. Food and nutrition security remain fragile and subject to natural and economic shocks in Zimbabwe, chronic under nutrition remains relatively high, albeit some improvements. Dietary diversity is generally poor and consumption of protein is insufficient. Only 11 percent of Zimbabwean children 6-23 months receive a minimum acceptable diet. One-third of Zimbabwe‘s children are stunted or short for their age, while the Central Statistics Office estimates that 70% of small scale farmers are living in poverty (www.zimstat.co.zw). JDKMS and M-apps can be used for the successful implementation of the Food Security and Nutrition Sector of the ZimAsset. Zim-Asset is the economic blue print that was formulated in 2013 by the ZANU (PF) led government. The ZimAsset policy consists of four clusters, namely Food Security and Nutrition; Social Services and Poverty Eradication; Infrastructure and Utilities; and Value Addition and Beneficiation. A successful implementation of the Food Security and Nutrition Sector, will lead to the realisation of the economic growth and poverty reduction. Outputs of this successful implementation of this sector will enable other sectors such as Social Services and Poverty Eradication, and Value Addition and Beneficiation. IJTRD | Jan-Feb 2017 Available [email protected]

II.

STATEMENT OF THE PROBLEM

Most smallholder farming systems are much less productive and profitable than they could be. The reasons include lack of access to inputs and credit, and the inability to bear risks. . Another major contribution is the information and skills gap that constrains the adoption of available technologies and management practices, or reduces their technical efficiency when adopted (World Bank, 2007). Public extension programs are often underfunded, suffer from weak agricultural research and lack adequate contact to farmers. A further problem is the lack of coordination along the agricultural value chain from farm inputs to food processing, which increases the cost of production and lowers revenue for farmers. Farming is becoming a more time-critical and informationintense business. A push towards higher productivity will require an information-based decision-making agricultural system. Farmers must be able to get information at the right time and place [1]. The list of potential benefits, (McNamara, 2009) covers numerous aspects of extension and agriculture development: 1. 2. 3. 4. 5. 6. 7.

Increasing smallholder productivity and incomes Making agricultural markets more efficient and transparent Linking poor farmers to urban, regional and global markets Improving services and governance for the rural poor Promoting – and including smallholders in – agricultural innovation Helping farmers manage a range of risks Improving land and natural resource management and addressing environmental

In Zimbabwe currently there are no Agricultural Knowledge Management Systems or applications in place. There should be an optimization of Java Developed knowledge Management Systems so as to enhance total Agricultural output efficiency but also should be straightforward enough that it can be easily understood by all the Agricultural stakeholders. The lack of Agricultural Knowledge Management Systems has been the most contributing factor of food insecurity, because as seasons have been shifting, most famers have not been able to acquire recent detailed information and they have not been equipped with the relevant knowledge, of when best to plough, as well as the type of crops to grow. The lack of the integration of Agricultural Knowledge Management Systems and M-apps also represents a significant financial burden for the Economy. On average, the government is losing millions of dollars every agricultural season through subsidising or supplying of free inputs to farmers. According to the Zimbabwean Financial Gazette, ZIMBABWE will be forced to import over one million tonnes of the staple grain at a 424

International Journal of Trend in Research and Development, Volume 4(1), ISSN: 2394-9333 www.ijtrd.com cost of more than US$200 million to avert hunger in most parts of the country following a disastrous 2014/2015 summer cropping season. Mobile communications technology has become the world‘s most common way of transmitting voice, data, and services, and no technology has ever spread faster. At the end of 2010 there were 5.25 billion cellular telephone subscriptions worldwide. By 2015 the number of mobile phone connections is expected to exceed the global population, (Source: Portio Research 2011.) The discourse on mobile technologies in agriculture is part of a wider debate on ICT and mobile technology in development, which has received significant attention over past few years. Enthusiastic studies find mobile phones do have a multidimensional positive impact on sustainable poverty reduction and identify accessibility as the main challenge in harnessing the full potential, (Silarszky et al., 2008). More critical political economists, (Leye, 2009) contest the assumption that technologies are autonomous forces or independent variables causing change in every domain of human life. Pointing to the importance of socioeconomic, cultural, political, and institutional factors, they believe rather that ICT reinforces existing dependencies, and call for the examination of crucial matters of control, cost, selection, and utilization. Poverty is a multidimensional phenomenon, and lack of access to information and communication can exacerbate its causes. ICT is not an end in itself but as an important enabler of ZimAsset. Instead of IT-driven approaches, let us call for appropriate policies and for a careful coordination and integration in other development strategies for ICT to create a positive overall impact on Agriculture. Mobile penetration in rural areas is also growing strongly (International Telecommunication Union ITU, 2010). Survey presented an inventory of current mobile applications in agriculture and assesses the potential of mobile apps‗to strengthen existing development efforts. Mobile communications technology has quickly become the world‘s most common way of transmitting voice, data, and services in the developing world. Given this dramatic change, mobile applications (m-apps) in general and mobile applications for agricultural and rural development (m-ARD apps) in particular hold significant potential for advancing development. They could provide the most affordable ways for millions of farmers to access information, markets, finance, and e-governance systems previously unavailable to them. M-apps are software designed to take advantage of mobile technology and can be developed for technology besides mobile phones. But mobile phones have many key advantages: affordability, wide ownership, voice communications, and instant and convenient service delivery. As a result, there has been a global explosion in the number of m-apps, facilitated by the rapid evolution of mobile networks and by the increasing functions and falling prices of mobile handsets. M-apps are markedly different in developing countries because they typically run on second-generation (2G) phones rather than smartphones, which are far more common in developed countries. Using M-apps and Java Developed knowledge Management Systems, Zimbabwe can become a bread basket again for SADC. Thus the Government of Zimbabwe stands to benefit substantially from the adoption and integration of Java

IJTRD | Jan-Feb 2017 Available [email protected]

Developed knowledge Management Systems and M-apps. For instance, the US$200 million used in 2015 to import staple grain can be re-directed to the re capitalisation of the Industrial and Manufacturing Sectors. III. 1. 2. 3. 4.

OBJECTIVES

To improve agriculture supply chain integration through the mobile applications To provide market information on farming websites To increase access to extension services, and facilitating market links To enhance Food Security and Nutrition, leading to the economic recovery of Zimbabwe through the use and integration of Java Developed Management Systems and M-apps. IV.

LITERATURE REVIEW

The researchers evaluated papers and books on the research topic so as to gain more understanding of the subject as well as come up with sound objectives for this research. Literature had to be reviewed also so that the researchers could identify the research gap and come up with a relevant study. This literature review evaluated research papers by other researchers on the research topic. The researcher explored the three dominant themes of the research objectives: mobile applications, mlearning mfarming and support services. The researcher evaluated research papers covering infrastructures and resources needed for mfarming activities being engaged by different countries. A. What are mobile applications? With mobile handsets being used in nearly every country and community, the development of applications for them offers uses that extend well beyond voice and text communications. Mobile applications for agricultural and rural development (mARD apps) could provide the most economic, practical, and accessible routes to information, markets, governance, and finance for millions of people who have been excluded from their use. B. Why mobile applications? Mobile phones: 1. Are owned by more people. 2. Provide delivery in an instant, more convenient way. 3. Can deliver personalized information to individual owners. 4. Are cheaper to deploy. 5. Provide other functions such as voice communication. Additionally, most M-apps can be replicated across different mobile interfaces and devices, such as SMS phones, mobile browsers, smartphones, and tablets. This is so for the most challenging part of developing m-apps involves their common backend and infrastructure—especially if integration between databases is required. C. Extension services Applications discussed under this category covers communications required to transfer and exchange knowledge and experiences to and among farmers, to facilitate the dissemination of information from research and extension agencies to farmers. This information flow addresses the significant skills deficit among small producers, and offers the potential to reach many more farmers than relying on traditional extension channels only.

425

International Journal of Trend in Research and Development, Volume 4(1), ISSN: 2394-9333 www.ijtrd.com

Figure 1: Information requirements and business processes offering opportunities for mobile applications along the value chain Mobile projects in agriculture extension can be clustered in two broad categories: a. mLearning 1. 2. 3. 4.

Transfer of general knowledge on farming techniques and trends, Information on plants and varieties and how to grow them, etc. Queries to a database. More interactive forms also offer possibilities for exchanging experiences among farmers.

b. mFarming Individual decision-support systems and services based on localized information, i.e. delivering spatial information based on microclimatic patterns, soil and water conditions throughout the farming seasons do as to inform farmers to optimize plant growth. Essentially, this is about making some key elements of precision farming available to small producers. mFarming requires remote sensing instruments and GIS. It can also involve advice systems such as remote diagnosis of diseases by experts. At the same time farmers can benefit from Java Developed Knowledge Management Decision Support Systems (JDKMDSS), since JDKMDSS can use analytical calculations or modelling methods evaluating and selecting the best possible IJTRD | Jan-Feb 2017 Available [email protected]

decisions in Agriculture, thus reducing the probability of uncertainty. These JDKMDSS will be used to evaluate the recent dynamic weather changing conditions, new technology and any changes in the fields. Thus equipping farmers with knowledge of being able to make or choose proper farm management techniques. D. market information and interaction This category clusters information flows required to coordinate the procurement and distribution of produce along the value chain. The use of mobile technology is expected to improve market transparency and efficiency and strengthen the farmers‗position as sellers of commodities. 1.

2.

Market information: This includes price information systems (e.g. market prices of different inputs and agricultural commodities in different trading locations). Trade facilitation / trading platforms: trading systems and platforms to identify best sale/buy opportunities and commodity exchange platforms.

Sustainable development of farms requires the development of farming systems that contribute to the increase of farmer‘s income, reaching socially acceptable levels, the reduction of soil erosion and the improvement of physical and biological 426

International Journal of Trend in Research and Development, Volume 4(1), ISSN: 2394-9333 www.ijtrd.com soil fertility(Ten Berge et al. 2000; Kropff et al. 2001). Today it is important for every farmer to know how much and what kind of agricultural production to produce; i.e. what areas of agricultural production should be developed to be able to meet complicated technological and environmental requirements and not to exceed environmental pollution norms as well as obtain the greatest possible profit. The methods of linear programming are often applied when solving such optimization problems in agriculture, (Annetts and Audsley 2002; Xiang et al. 2004). Thus, JDKMDSS should integrate agro-environmental knowledge of specialists with a farm management model, so as to combine environmental and economical objectives. Therefore the importance of reducing the negative impact of chemical plant protection products and fertilizers to the environment. At the same time JDKMDSS can also be helpful to a farmer to make scientifically based decisions on strategic and operative farm management in real time. E. Support services and systems Quality control: communications between sellers and buyers, producers and consumers, to facilitate exchange of quality of product (e.g. grading) and non-economic values as external inputs to market pricing (e.g. certification of fair trade products, adherence to quality standards, ecological footprint, verification of origin of product). Logistics and business process management: Applications that facilitate sound business processes in rural areas (e.g. transporting agricultural commodities, tracking goods, organizing seller/buyer accounts). Financial services: Communications and processes to provide financial services such as payment or insurance to rural farmers and agents involved in the agriculture value chain. Applications in this area particularly address the issues of distribution, outreach and business processes that enable dealings with clients in rural areas. V.

OPTIMIZATION OF JAVA DEVELOPED MANAGEMENT SYSTEMS

Figure 3: Complex mAgriculture initiatives VI.

VIABILITY AND SUSTAINABILITY OF MAGRICULTURE SERVICES

Figures 2 and 3 illustrate the various parties involved in mAgriculture services. Together, they represent the value chain of a particular mAgriculture service, i.e. the collection of participants, their relations and steps involved in the design of the solution components, content production, promotion and delivery process, including support and training for the end user. To turn mAgriculture initiative into a viable and self-sustaining product, two critical sets of criteria must be addressed: 1.

2.

The first set of criteria focuses on the end user of the service and is mainly about usefulness of the product (value for money); The second set of criteria addresses the needs and incentives of the other players in the value chain of the service

mAgriculture projects are built on the opportunities provided by increasing use of mobile phones by farmers in developing countries. The expectation is that they increase outreach and at the same time lower costs. However, if they are to capitalize fully on the technical opportunities, mAgriculture projects have to benefit end users in terms of convenient and valid solutions to some of their every-day challenges. The following criteria help to evaluate this aspect: 1.

Availability and Accessibility of service: language barriers, literacy barriers (both general and technical), availability across a large diversity of handsets, skills and education level of prospective users, training and support requirements, time to learn and use, timeliness of service

2.

Value and ‘richness’ of content, value for money: Richness and value of content provided or relevance of task performed, content generation and development over time, mAgriculture accurate local content that is really usable, responsiveness to user demands, affordability (in particular considering farmers‗ price sensitivity)

3.

Impact: impact achievements (e.g. improved performance or outreach); cost savings or increased operational effectiveness (e.g. where it is used to organize business processes);

Figure 2: Basic mAgriculture services and agents involved

Most mobile apps focus on improving agriculture supply chain integration and have a wide range of functions, such as providing market information, increasing access to extension services, and facilitating market links. Users are also diverse, including farmers, produce buyers, cooperatives, input IJTRD | Jan-Feb 2017 Available [email protected]

427

International Journal of Trend in Research and Development, Volume 4(1), ISSN: 2394-9333 www.ijtrd.com suppliers, content providers, and other stakeholders who demand useful, affordable services. These supply chain integration applications could provide significant economic and social benefits—among them, creating jobs, adding value, reducing product losses, and making developing countries more globally competitive. But the potential development impact of mobile apps mainly lies in their ability to provide access to useful, relevant information and services. Quantitatively, the most widely used mobile apps provide access to valuable information a crucial functions because asymmetrical access to information is a weakness of rural markets in developing countries. Kenyan farmers who use the app DrumNet, for example, have seen their incomes rise by a third due to the service‘s comprehensive system of price negotiation, contracting, and other value chain support. In other developing countries like Zimbabwe that have adopted the use of mobile applications in farming they are immensely enriching themselves. Mobile apps also provide farmers and rural residents with timely access to extension services, such as advice on agricultural production, marketing, and technology, food security, and nutrition. Sri Lanka‘s e-Dairy helps farmers earn up to $262 more a year for each of their calves by providing veterinary and extension services delivered by mobile phones. Such applications also strengthen market links when used to improve production distribution and traceability. Tea growers in Kenya have reported average income growth of 9 percent about $300 a year by using Virtual City‘s production measuring, recording, and traceability functions. In addition, mobile apps have expanded access to finance and insurance products in rural areas. Applications like M-PESA in Kenya and ecocash, telecash have gained acceptance as safe, easy ways to receive payments and store money. Also ecofarmer, users have agricultural insurance and products have seen their production increase by an average of more than 50 percent, or about $150 a year. Recently, the importance of modelling and optimization-based decision support has significantly increased in agriculture [10] (Shim et al. 2002; Pranevičius and Kurlavičius 2003; Strauss et al. 2008). Thus Java Developed Optimised Management System Model (JDOMSM) can be introduced for optimising the production structure in agricultural enterprises, reflecting technological, economical, and organizational aspects of agricultural production and estimates initial farm conditions. The main variables of plant growing applied in the model are as follows: the areas used for growing different varieties of agricultural multi-purpose crops; the quantities of various kinds of agricultural production, the quantities of fertilizers, pesticides and other chemical materials for plant cultivation; and the quantities of agricultural equipment, technical and power resources necessary to perform various works. The main variables of animal husbandry are as follows: animal quantities in terms of different breeds, age and purpose groups; the capacity of the main buildings; and the quantities of various fodders. Production expenses for different kinds of agricultural products, income and the scope of production, pollutant quantities and other items could be used as variables of the model[11] (Sustainable agricultural development: Knowledge‐ based decision support - Department of Informatics, Lithuanian University of Agriculture). Decision Support System (DSS), which estimates the efficiency of possible decisions and reduces uncertainty in management by means of analytical calculation or modelling methods, can help evaluate and select the best possible decisions [12] IJTRD | Jan-Feb 2017 Available [email protected]

(Kaklauskas et al. 2005). This means that technological, organizational, economic and environmental requirements should also be estimated in the JDOMSM of agricultural production. Specific number and content of limitations depends on the quantity of existing initial data, characterizing the status of an agricultural enterprise and the vision of farm development. Optimization of JDMS goal is to find the values of variables that conform to the greatest possible gain under initial input conditions and satisfied limitations. The JDOMSM coefficients are the arable land, pastures and meadows existing in the farm, parameters of technological efficiency, recommended maximum share of grain crops and minimum share of perennial grasses in the structure of crops, forecasted productivity of crops, the need for separate fodder for an animal per year, forecasted expenses according to the types of production, and forecasted selling prices. The problem of poor Production planning can be solved by using a modified simple method. Firstly a possible production structure with non-negative variable inputs is searched. The user will receive a set of recommendations necessary for changing the corresponding initial input data in order to eliminate contradictions. The calculation will then be terminated and returned for the input of correct initial data. Thus a consistent system and an initial possible farm production plan with non-negative values of variable inputs are found, and the cycle of plan improvement is implemented. These steps can be continuously repeated, improving the plan until the highest farm effectiveness is achieved. Agritex extension officers can use this information to analyse the results, and be in a position to use the obtained values of variable inputs to identify the idea crops for ideal lands, thus maximising production. At the same time the extension officers will also be in a position to know the type of animals to be kept in a particular area and the number that can be accommodated at that particular area. Hence the extension officers can recommend resources to be used and the amounts to be, so that the farm would get the biggest benefit under the indicated environmental constraints and technical-economic coefficients entered. Any additional values of additional inputs to the JDOMSM, obtained during optimization, will indicate the resources being underutilized or resources the farm might be lacking. The constructed linear JDOMSM serves as a constituent of the JDKMDSS. The JDOMSM includes sensitivity analysis of different market conditions, different resource and improved or new ecological technology usage. VII.

INTEGRATION OF JDOMSM AND CONCLUSION FORMATION

To help with proper land redistribution the additional values of variable inputs, acquired during calculations, will indicate those farm lands that are not being fully utilized. The Ministry of Lands and Resettlement can use this information for land allocation. The newly allocated and resettled farmers can be encouraged to use the information from the JDOMSM. This is because it may take time for already resettled farmers to implement the structural change in farm production, due to the limitations in crop rotation and the production lagging in animal husbandry because of specific features of animal herd formation. DSS must satisfy users‘ requirements as much as possible; the data it contains must be easily updated. Convenient on-line systems serve the purpose, (Kaklauskas et al. 2007). Known

428

International Journal of Trend in Research and Development, Volume 4(1), ISSN: 2394-9333 www.ijtrd.com facts from the JDOMSM can be stored in a Knowledge base for future reference by the farmers, whenever a problem arises. For the farmers to be able to understand the knowledge, the ―Ifthen‖ style rules are applied to the processing of structured facts, and Agritex extension officers can go around helping out in defining the rules to the farmers. Java User functions will be used to create the knowledge base, and it is important that when applying the set of rules that a plan of action should be presented, as well as the conclusions concerning any possibilities of change. The knowledge base should also accommodate the knowledge of various different domains such as veterinary for us to have a sound JDKMDSS. VIII.

OPTIMIZATION AND JDKMDSS

The need to effectively integrate decision-making tasks, together with knowledge representation and visualisation tasks, inference procedures that model an expert‘s thinking process, has strained research attempts to integrate DSS with Knowledge Systems, (Kaklauskas et al.2008; Kaklauskas and Zavadskas 2007). The framework of the integrated optimization and knowledge-based DSS contains database, knowledge base, model formation, optimization, simulation and conclusion formation modules (Fig. 2). Program modules operate in the server, which is easily accessible by users via the Internet. DSS interface contains 3 main parts: (1) a universal form, which helps to get information from the user; (2) a mechanism for communication with expert system that forms conclusions; (3) a universal form, presenting the results of the system work on the user‘s screen, (Sustainable agricultural development: Knowledge‐ based decision support - Department of Informatics, Lithuanian University of Agriculture).

Figure 4: The architecture of farm management knowledgebased DSS The farmer will be asked questions by the system and he or she will enter the initial data about the farm. The data entered is processed using real-time regime and he or she will receive the results of data consistency analysis. Constants and rarely changing data, similar to many farms of the region, are stored in the system knowledge base. These are different fodder nutritious indices, fodder demand for different age animal groups, parameters of technological parameters, productivity of various agricultural machinery and other characteristics. These coefficients are specified, entered and modified by Agritex extension officers. Analytical information, collected during optimization and simulation, can also be stored in the knowledge base. Forecasted market prices, standard expenses can be entered by a farmer and calculated using the data from the knowledge base of the distant server. IJTRD | Jan-Feb 2017 Available [email protected]

This sets the interaction of model formation actions and events. On the farmer‘s request, the model formation system automatically forms a constraints system; therefore, the farmer can possess no knowledge about the composition of the mathematical model of the farm. In this case, coefficients of the model are specified by the system of model formation according to the ―if-then‖ style rules, stored in the knowledge base. If a farmer decides to keep animals of certain breeds or to apply new feeding technologies and scientist-recommended ratios, corresponding coefficients of the model are automatically selected from the knowledge base according to expert suggestions. A more experienced farmer or an Agritex extension officer will have a possibility to choose a desired frame of a farm model with the help of the menu, and to modify it in different ways as well as develop and evaluate the desired variants of farming. Besides, the program of model formation allows the expert to enter new desired constraints and set digital values of coefficients. The knowledge base is integrated into the JDKMDSS. An Agritex expert will have a possibility to review and supplement the set of production rules and facts. Having applied the set of production rules to the given facts and modelling results within the module of decision analysis and inference, conclusions and suggestions are formed and the results obtained are presented to the farmer. It is necessary to access the benefit of any foreseen changes, if one production is anticipated to be produced than another. The proposed system is convenient for anticipatory assessment of foreseen changes as it forms conclusions according to the production rules and presents comprehensive explanations on the screen or in print on a farmer‘s request. According to the values of major, additional and dual variables obtained in the course of calculation, the JDKMDSS forms conclusions and proposals on the possibility to change one type of fodder with another, indicates the deficiency of enterprise resources that impede production development most, submits proposals on material-technical resources that are necessary for the development of profitable production. These conclusions can be send out to affected farmers through M-apps. The majority of farmers make decisions without considering how they do that; thus it is not easy to use their experience while improving or developing a new JDKMDSS. Most peasant farmers in Zimbabwe‘s rural areas seek for efficient work with minimum usage of formal apparatus and in complicated cases they address consulting services. Although farmers will acknowledge the usefulness of JDKMDSS, they are often disappointed while trying to understand its functioning, thus farmers‘ training is integral part of JDKMDSS implementation. The farmer should be able to pose a question clearly and to perceive the information presented by the system correctly in order to use the JDKMDSS conclusions. The JDKMDSS will enable to perform thorough qualitative and quantitative assessment of foreseen changes in agricultural production structure and to make effective decisions; it also reduces decision-making costs. It is advisable to introduce the system in agricultural enterprises and consulting services. With a constant supply of the latest knowledge on plant growing, animal husbandry and agro-business, the intelligence and usefulness of the system will increase. One needs a doctor when in pain and similarly DSS is needed when a farmer faces the changes in accustomed environment or unclear situation and experiences uncertainty. DSS serves as a tool to improve decisions and helps a farmer to select the most

429

International Journal of Trend in Research and Development, Volume 4(1), ISSN: 2394-9333 www.ijtrd.com efficient production plan, to make economic-based decisions that enable him to reduce production costs and preserve nature [16] (Sustainable agricultural development: Knowledge‐ based decision support - Department of Informatics, Lithuanian University of Agriculture). Thus, JDKMDSS is useful to the farmer when he experiences the lack of knowledge and the need for help. While new technologies appear and economic environment changes, the need for JDKMDSS increases. JDKMDSS is needed when a farmer faces the changes in accustomed environment or unclear situation and experiences uncertainty. JDKMDSS will serve as a tool to improve decisions and to help a farmer to select the most efficient production plan, so as to make economic-based decisions that enable him to reduce production costs and preserve nature. Farm JDKMDSS is a convenient tool that will help to acquire knowledge on agricultural manufacturing processes and to train farmers. After having found a solution applicable to a certain situation in JDKMDSS, there is a probability of the farmer remembering it in future if the same scenario presents itself again. This in a way will help a farmer to acquire new experience, through the use of the JDKMDSS. IX.

FUTURE RECOMMENDATIONS

Mobile apps offer dynamic, interdisciplinary, and innovative services to rural farmer‘s residents in developing countries the case of Zimbabwe. This report offered snapshots of the field‘s evolution but provides policymakers and development practitioners with insight into its significant potential. Mobile Platforms can facilitate interactions among ecosystem players,

increase access to users, provide technical standards, and incorporate payment mechanisms. The hyper-local nature of mobile apps makes scaling up challenging for providers. It is crucial for providers to leverage existing information resources, and providers who can aggregate and customize content from different sources will have an advantage. Governments and donors can be immensely helpful by making data publicly available and ensuring that the data are as accurate and granular as possible. Despite various challenges, some m-apps are achieving scalability, reliability, and sustainability, for instance ecofarmer platform. Government and donors play a critical role in helping m-apps achieve sustainability by covering initial capital costs. M-apps that can achieve low operating costs are also more likely to be sustainable. The results of the JDOMSM indicate that the introduction of environmental limitations leads to a lower net farm income and better performance with all ecological indicators. Model outputs help decision makers to predict the influence of their decisions within a long-term plan Below are some mobile applications that are in use in the United States of America, if adopted in Zimbabwe the cluster of Food security and nutrition can be immensely developed thus ensuring that the nation is well fed. A hungry man is an angry man and as a peace loving nation we will feed the nation to avoid mutiny. X.

APPS FOR AGRICULTURE

Commodity Prices. Track corn, soybeans, wheat, cotton, lean hogs, live cattle, feeder cattle and more. The app has a clean interface and is simple to operate. As one Android Market reviewer commented, it is "dead-on right all day, every day." Cash Grain Bids. Simply input your ZIP code to find out cash bids and base levels in your area. Get bids from the five elevators closest to you.

Weather Underground. There are many weather apps, and this one holds up as well as any of them. Lots of information is available, including temperature, visibility and humidity. View hourly and seven-day forecasts, too. Livestock Manager. A lot of mobile apps are targeted to livestock producers. This particular one allows users to track various information about their animals, including parentage, transport information, medicine administration and more. Farm Futures. The power of Farm Futures magazine in an app! This enhanced mobile presentation of the leading management oriented farm magazine features enhanced user-customized markets, market commentary, news and audio updated every business day.

TractorHouse , the most trusted name in information processing for the construction, farming, trucking, aviation, and computer industries for over 30 years. Features: - Select category - Select manufacturer – scroll or search - Select model or model group – scroll or search

CONCLUSION Java programmed expert systems and knowledge management systems are crucial in any economy if it is to survive the current dynamic economic and agrarian reforms. IJTRD | Jan-Feb 2017 Available [email protected]

Mobile applications for agricultural and rural development and JDKMS offer innovative, dynamic, interdisciplinary services. These new services could raise incomes and create more opportunities for people in rural and underserved communities in developing countries as well as stakeholders in Zimbabwe. There is need for rapid development and 430

International Journal of Trend in Research and Development, Volume 4(1), ISSN: 2394-9333 www.ijtrd.com deployment of these systems and platforms to combat or safeguard the farmers against uncertainties. The observations in this report provided a synopsis of the evolution of technology to enable agrarian reforms in developing nations particularly Zimbabwe. However, policymakers and development practitioners must show great support and work hand-in-hand with mobile network providers and farmers to effectively collect data from farmers in all 5 farming regions in Zimbabwe. ZimAsset cluster of Food Security and Nutrition can be fulfilled if teamwork prevails. References 1. 2.

3. 4.

5. 6.

Mobile Applications in Agriculture, Fritz Brugger / [email protected] McNamara, K., 2009. ―Mobile Applications in Agriculture and Rural Development: Framing the Topic, and Learning from Experience.‖ World Bank, Washington, D.C. Portio Research. 2011. ―Mobile Factbook 2011.‖ www.portioresearch.com. Leye, Veva (2009), 'Information and Communication Technologies for Development: A Critical Perspective', Global Governance, 15 29-35. Silarszky, Peter, et al. (2008), 'The Role of Mobile Phones in Sustainable Rural Poverty Reduction', 25. International Telecommunication Union ITU (2010), Measuring the Information Society 2010, (Geneva, Switzerland: ITU) 124.

IJTRD | Jan-Feb 2017 Available [email protected]

7.

8.

9. 10. 11.

12.

13. 14. 15. 16. 17.

Kemibaro, Moses. 2011. ―PesaPi—An Open Source API for Safaricom‘s M-Pesa in Kenya.‖ http://www.moseskemibaro.com/2011/05/25/m-pesapian-open-source-api-for-safaricoms-m-pesa-in-kenya. de Silva, Harsha and Dimuthu Ratnadiwakara (2008), 'Using ICT to reduce transaction costs in agriculture through better communication: A case-study from Sri Lanka', mimeo, 20. Agricultural sustainability: concepts, principles and evidence - Jules Pretty - February 2008 Environment and Natural Resource Management IFAD‘s Growing Commitment Good Agricultural Governance for Transition to Sustainable Production Intensification in Smallholder Farming – Subash Dasgupta and Indrajit Roy Sustainable agricultural development: Knowledge‐ based decision support - Department of Informatics, Lithuanian University of Agriculture – pages 4 – 5, 6 – 14 Putting Farmers First In Sustainable Agriculture Practices – Hira Jhamtani 3rd World Network Extension's role in sustainable agricultural development – Niels Röling and Jules N. Pretty Investments in Sustainable Agricultural Intensification Agriculture Investment Sourcebook –World Bank Recent Developments in Data Mining and Agriculture A. Mucherino and G. Ruß http://www.zw.one.un.org/togetherwedeliver/zundaf

431