Your search found 9 records
1 Mitchell, T. 2005. Web mapping illustrated. Sebastopol, CA, USA: O'Reilly Media. 349p.
(Location: IWMI HQ Call no: 526.0285 G000 MIT Record No: H043095)
2 Harzing, A-W. 2011. The publish or perish book: your guide to effective and responsible citation analysis. Melbourne, Australia: Tarma Software Research. 246p.
(Location: IWMI HQ Call no: 808.02 G000 HAR Record No: H044631)
(0.40 MB)
This book is a companion to the software program Publish or Perish (PoP). PoP was designed in the first instance to calculate citation metrics for a variety of purposes. Academics that need to make their case for tenure or promotion will find PoP useful to create reference groups and show their citation record to its best advantage. When evaluating other academics, PoP can be used as a 5-minute preparation before meeting someone you don’t know, to evaluate editorial board members or prospective PhD supervisors, to write up tributes (or laudations) and eulogies, to decide on publication awards and to pre-pare for a job interview. Deans and other academic administrators will find PoP useful to evaluate tenure or promotion cases in a fair and equitable way.
PoP can also be used to assist when you are uncertain which journal to submit it to. You can use it to get ideas of the types of journals that publish articles on the topic you are writing on and to compare a set of journals in terms of their citation impact. Once you have decided on the target journal, it can also help you to double-check that you haven’t missed any prior work from the journal in question.
PoP can help you to do a quick literature review to identify the most cited articles and/or scholars in a particular field. It can be used to identify whether any research has been done in a particular area at all (useful for grant applications) or to evaluate the development of the literature in a particular topic over time. Finally, PoP is very well suited for doing bibliometric research on both authors and journals.
PoP can also be used to assist when you are uncertain which journal to submit it to. You can use it to get ideas of the types of journals that publish articles on the topic you are writing on and to compare a set of journals in terms of their citation impact. Once you have decided on the target journal, it can also help you to double-check that you haven’t missed any prior work from the journal in question.
PoP can help you to do a quick literature review to identify the most cited articles and/or scholars in a particular field. It can be used to identify whether any research has been done in a particular area at all (useful for grant applications) or to evaluate the development of the literature in a particular topic over time. Finally, PoP is very well suited for doing bibliometric research on both authors and journals.
3 Asian Development Bank (ADB). 2018. Internet plus agriculture: a new engine for rural economic growth in the People’s Republic of China. Manila, Philippines: Asian Development Bank (ADB). 53p. [doi: https://doi.org/10.22617/TCS189559-2]
Agricultural development ; Information services ; Internet ; Rural economics ; Economic growth ; Electronic commerce ; Agricultural products ; Supply chain ; Development projects ; State intervention ; Policies ; Infrastructure ; Investment ; Constraints ; Corporate culture ; Farmers ; Agricultural extension ; Developing countries ; Public services ; Models / China / Gansu / Hubei / Shandong / Yunnan / Zhejiang
(Location: IWMI HQ Call no: e-copy only Record No: H049034)
https://www.adb.org/sites/default/files/publication/455091/internet-plus-agriculture-prc.pdf
https://vlibrary.iwmi.org/pdf/H049034.pdf
https://vlibrary.iwmi.org/pdf/H049034.pdf
(3.48 MB) (3.48 MB)
4 Al-Ali, A. R.; Al Nabulsi, A.; Mukhopadhyay, S.; Awal, M. S.; Fernandes, S.; Ailabouni, K. 2020. IoT-solar energy powered smart farm irrigation system. Journal of Electronic Science and Technology, 100017 (Online first) [doi: https://doi.org/10.1016/j.jnlest.2020.100017]
Solar energy ; Irrigation systems ; Farms ; Technological changes ; Internet ; Fuzzy logic ; Renewable energy ; Soil water content ; Humidity ; Temperature ; Pumps
(Location: IWMI HQ Call no: e-copy only Record No: H049587)
https://www.sciencedirect.com/science/article/pii/S1674862X20300148/pdfft?md5=9014d978744b36cf7b27c8a422016a22&pid=1-s2.0-S1674862X20300148-main.pdf
https://vlibrary.iwmi.org/pdf/H049587.pdf
https://vlibrary.iwmi.org/pdf/H049587.pdf
(3.48 MB) (3.48 MB)
As the Internet of things (IoT) technology is evolving, distributed solar energy resources can be operated, monitored, and controlled remotely. The design of an IoT based solar energy system for smart irrigation is essential for regions around the world, which face water scarcity and power shortage. Thus, such a system is designed in this paper. The proposed system utilizes a single board system-on-a-chip controller (the controller hereafter), which has built-in WiFi connectivity, and connections to a solar cell to provide the required operating power. The controller reads the field soil moisture, humidity, and temperature sensors, and outputs appropriate actuation command signals to operate irrigation pumps. The controller also monitors the underground water level, which is essential to prevent the pump motors from burning due to the level in the water well. The proposed system has three modes of operations, i.e. the local control mode, mobile monitoring-control mode, and fuzzy logic-based control mode. For the purpose of the proposed system validation, a prototype was designed, built, and tested.
5 Keswani, B.; Mohapatra, A. G.; Keswani, P.; Khanna, A.; Gupta, D.; Rodrigues, J. J. P. C. 2020. Improving weather dependent zone specific irrigation control scheme in IoT and big data enabled self driven precision agriculture mechanism. Enterprise Information Systems, 23p. (Online first) [doi: https://doi.org/10.1080/17517575.2020.1713406]
Precision agriculture ; Irrigation scheduling ; Decision support systems ; Internet ; Neural networks ; Soil water content ; Forecasting ; Crops ; Farmland ; Weather ; Models / India / Bhubaneswar
(Location: IWMI HQ Call no: e-copy only Record No: H049681)
(3.53 MB)
Precision agriculture involves manipulation of variations in field productivity, maximization of income, scale backing of wastes, and minimizing of the impact on surroundings using automated machine-controlled information assortment and documentation. This work focuses on the efficient control of farm irrigation by exploiting the capabilities of Internet of Things (IoT) and Big Data-based Decision Support System (DSS) to generate adequate valve control commands. Three varieties of prediction techniques such as Deep Neural Network (DNN), Random Forest (RF) and Resilient Back-Propagation Neural Network model are tested to predict soil Moisture Content (MC) in one hour advance by considering 6 numbers of different sensors. The real-time data collection is performed using the proposed IoT node deployment strategy tested in the field. An integrated IoT-based DSS framework is proposed to accumulate 17 numbers of soil and environmental parameters to predict future variation of soil MC in 1 h advance. Further, Structural Similarity (SSIM) Index is used to visualize and maintain uniform MC all over the agriculture area during the entire cropping period. Site and zone specific irrigation control scheme is tested in the test site using fuzzy logic-based weather dependent model. The complete system architecture, deployment strategy and performance of the proposed IoT-based DSS mechanism is discussed in this article.
6 Torres, A. B. B.; da Rocha, A. R.; Coelho da Silva, T. L.; de Souza, J. N.; Gondim, R. S. 2020. Multilevel data fusion for the internet of things in smart agriculture. Computers and Electronics in Agriculture, 171:105309. [doi: https://doi.org/10.1016/j.compag.2020.105309]
Decision support systems ; Internet ; Agriculture ; Irrigation ; Soil moisture ; Evapotranspiration ; Energy consumption ; Linear models ; Sensors ; Crops ; Cashews ; Coconuts / Brazil / Paraipaba
(Location: IWMI HQ Call no: e-copy only Record No: H049724)
(7.91 MB)
The Internet of Things (IoT) aims to enable objects to sense, identify, and analyze the world, but to achieve such goal cost-effectively, it should involve low-cost solutions. That implies a series of limitations, such as small battery life, limited storage capabilities, low accuracy, and imprecise sensors. Data fusion is one of the most widely used methods for improving sensor accuracy and providing a more precise decision. Therefore, we propose Hydra, a multilevel data fusion architecture, to improve sensor accuracy, identify application target events, and make more accurate decisions. Hydra is composed of three layers: low-level (sensor data fusion), medium-level (events and decision making), and high-level (decision fusion based on multiple applications). In partnership with Embrapa (Brazilian Agricultural Research Corporation), we instantiated Hydra for the smart agriculture domain, and we also developed two applications aiming smart water management. The first application goal was to determine the need for irrigation based on soil moisture levels, and the second ascertained the adequate irrigation time by estimating the crop’s evapotranspiration (rate of water evaporation by the soil and transpiration by plants). We performed a set of experiments to assess Hydra: (i) evaluation of methods to detect and remove outliers; (ii) analyze data resulting from the applications; (iii) the use of machine learning to create a new accurate evapotranspiration model based on the sensors data. The results indicate that a combination of the ESD method (Extreme Studentized Deviate) and WRKF filter (Weighted Outlier-Robust Kalman Filter) was the best method to identify and remove outliers. Moreover, we generated an evapotranspiration model using the SVM (Support Machine Vector) quadratic machine-learning model that produced values close to the evapotranspiration reference model (Penman-Monteith).
7 Mehrabi, Z.; McDowell, M. J.; Ricciardi, V.; Levers, C.; Martinez, J. D.; Mehrabi, N.; Wittman, H.; Ramankutty, N.; Jarvis, A. 2020. The global divide in data-driven farming. Nature Sustainability, 7p. (Online first) [doi: https://doi.org/10.1038/s41893-020-00631-0]
Agriculture ; Innovation ; Technology ; Mobile phones ; Data management ; Smallholders ; Farmers ; Farmland ; Households ; Infrastructure ; Internet / Africa / Asia / Latin America / Caribbean
(Location: IWMI HQ Call no: e-copy only Record No: H050061)
(1.62 MB) (1.62 MB)
Big data and mobile technology are widely claimed to be global disruptive forces in agriculture that benefit small-scale farmers. Yet the access of small-scale farmers to this technology is poorly understood. We show that only 24–37% of farms of <1 ha in size are served by third generation (3G) or 4G services, compared to 74–80% of farms of >200 ha in size. Furthermore, croplands with severe yield gaps, climate-stressed locations and food-insecure populations have poor service coverage. Across many countries in Africa, less than ~40% of farming households have Internet access, and the cost of data remains prohibitive. We recommend a digital inclusion agenda whereby governments, the development community and the private sector focus their efforts to improve access so that data-driven agriculture is available to all farmers globally.
8 Sangeetha, B. P.; Kumar, N.; Ambalgi, A. P.; Haleem, S. L. A.; Thilagam, K.; Vijayakumar, P. 2022. IOT based smart irrigation management system for environmental sustainability in India. Sustainable Energy Technologies and Assessments, 52(Part A):101973. (Online first) [doi: https://doi.org/10.1016/j.seta.2022.101973]
Irrigation management ; Technology ; Internet ; Environmental sustainability ; Renewable energy ; Irrigation systems ; Agriculture ; Neural networks / India
(Location: IWMI HQ Call no: e-copy only Record No: H051001)
(2.90 MB)
Food and clean water make agriculture a valuable asset to humanity, as it uses water to provide us with food. Environmental destruction and rapid population growth have had a massive effect on agriculture, a detrimental impact on the world's water supplies, and crucial for sustained development. To resolve the issue, implement the intelligent irrigation method using automated and Internet of Things (IoT) technologies. This study involves an intelligent agriculture management system to produce agricultural benefits and crop production. The hybrid remote-controlled device used the Global Positioning System (GPS) with Radial Function Network (RFN) was proposed to control the irrigated system, predict the temperature, maintain the air pressure, and reduced the humidity in water content. It uses IoT sensors and the Internet of Everything (IOE) environmental data for managing and monitors intelligent solar irrigation systems. The objective is agriculture intelligent by using automation and IoT technologies. It scientifically designed to perform tasks such as weeding, irrigation, sensing humidity, attempting to scare birds and livestock, maintaining surveillance, etc., to control the geolocation of devices remotely. As a result, the design is to achieve all of its goals in terms of water use; total running costs decreased labour, energy consumption, and productivity. It is found that proposed Radial Function Network achieved 0.7734f accuracy, 0.9834 of sensitivity, 0.8955 of hit rate and 0.77 of caching rate.
9 Carneiro, B.; Resce, G.; Laderach, P.; Schapendonk, F.; Pacillo, G. 2022. What is the importance of climate research? An innovative web-based approach to assess the influence and reach of climate research programs. Environmental Science and Policy, 133:115-126. (Online first) [doi: https://doi.org/10.1016/j.envsci.2022.03.018]
Climate change ; Research programmes ; CGIAR ; Food security ; Climate-smart agriculture ; Diffusion of information ; Innovation ; Internet ; Social media ; Digital technology ; Network analysis ; Text mining ; Stakeholders ; Policies
(Location: IWMI HQ Call no: e-copy only Record No: H051061)
https://www.sciencedirect.com/science/article/pii/S1462901122001058/pdfft?md5=ed4fd9f06b7706fcb16a0699d66ba94d&pid=1-s2.0-S1462901122001058-main.pdf
https://vlibrary.iwmi.org/pdf/H051061.pdf
https://vlibrary.iwmi.org/pdf/H051061.pdf
(7.72 MB) (7.72 MB)
Many parts of the world are increasingly experiencing the effects of climate change, making climate adaptation of rural livelihoods crucial to secure social and economic resilience. While the past two decades have witnessed a significant evolution in climate adaptation policy, evaluating the impact of climate science on policy has remained a challenge. This study employs the Digital Methods epistemology to explore the dynamics of agriculture-focused climate science and changes in attitude towards Climate Smart Agriculture (CSA) and climate change, using the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) as a case study. By considering online networks and narratives as evidence of “offline” influence, it effectively repurposes publicly available data from digital sources such as social media and websites by employing text mining and social network analysis to assess the influence and reach of the program among stakeholder at various levels. Results show that CCAFS has supported increased public awareness of CSA; that it actively engages with key actors within a network of stakeholders with more than 60 thousand members; that it has positively shifted the debate on climate adaptation among strategic partners through increased message alignment and space in the policy agenda; and that the program’s reach is potentially amplified to 5.8 M users on Twitter.
Powered by DB/Text WebPublisher, from
