A compendium of Technologies, Practices, Services and Policies for Scaling Climate Smart Agriculture in Odisha (India)

Stakeholders engaged in agricultural research for development (AR4D) are increasingly tackling risks associated with climate change in smallholder systems. Accordingly, development and scaling of climate-smart agriculture (CSA) are one of the priorities for all the organizations, departments and ministries associated with the farm sector. Having a ‘one-stop-shop’ compiled in the format of a compendium for CSA technologies, practices and services would therefore serve a guide for all the stakeholders for scaling CSA in smallholder systems. Bringing out a Compendium on Climate-Smart Agriculture (CSA) for Odisha, India was therefore thought of during the workshop on ‘Scaling Climate-Smart Agriculture in Odisha’ organized at Bhubaneswar on 18-19 July 2018 by International Rice Research Institute (IRRI) in collaboration with Department of Agriculture (DoA) & Farmers’ Empowerment, Indian Council of Agricultural Research-National Rice Research Institute (ICAR-NRRI), Orissa University of Agriculture and Technology (OUAT) & International Maize and Wheat Improvement Center (CIMMYT) under the aegis of CGIAR Research program on Climate Change, Agriculture and Food Security (CCAFS). The main objectives to bring forth this compendium are: to argue the case for agriculture policies and practices that are climate-smart; to raise awareness of what can be done to make agriculture policies and practices climatesmart; and to provide practical guidance and recommendations that are well referenced and, wherever possible, based on lessons learned from practical action. CSA programmes are unlikely to be effective unless their implementation is supported by sound policies and institutions. It is therefore important to enhance institutional capacities in order to implement and replicate CSA strategies. Institutions are vital to agricultural development as well as the realisation of resilient livelihoods.They are not only a tool for farmers and decision-makers, but are also the main conduit through which CSA practices can be scaled up and sustained. The focus in this compendium is on CSA and it’s relevant aspects, i.e., (i) technologies and practices, (ii) services, (iii) technology targeting, (iv) business models, (v) capacity building, and (vi) policies. The approaches and tools available in the compendium span from face-to-face technicianfarmer dialogues to more structured exchanges of online and offline e-learning. In every scenario it is clear that tailoring to local expectations and needs is key. In particular, the voice of farmers is essential to be captured as they are the key actors to promote sustainable agriculture, and their issues need to be prioritized. CSA practices are expected to sustainably increase productivity and resilience (adaptation), reduce Greenhouse Gases (mitigation), and enhance achievement of national food security along with sustainable development goals. CSA is widely expected to contribute towards achieving these objectives and enhance climate change adaptation. CSA practices have to be included in State’s Climate Policy as a priority intervention as the state steps up efforts to tackle climate change. Furthermore, emphasis shoud be laid on CSA training for a sustainable mode to enhance CSA adoption in the state hence the relevance of developing this document. The adaption of climate related knowledge, technologies and practices to local conditions, promoting joint learning by farmers, researchers, rural advisor and widely disseminating CSA practices, is critical. This compendium brings together a collection of experiences from different stakeholders with background of agricultural extension and rural advisory services in supporting CSA. The contributions are not intended to be state-of-the art academic articles but thought and discussion pieces of work in progress. The compendium itself is a ‘living‘ document which is intended to be revised periodically

Tanzania Country Climate Risk Profile Series, Kilolo District

The agricultural sector in Tanzania has been exposed to high climatic risks for the past several decades (Arce & Caballero, 2015). Experts and farmers assert that climatic risks including unpredictable rainfall, prolonged drought, and increased incidences of pests and diseases have resulted in declining agricultural productivity. Concomitantly, the rivers, streams, soils, and forests from which the rural poor build their livelihoods are on the verge of depletion. The situation has been further exacerbated by unstable commodity prices. Future climatic projections show that the climate trends are likely to worsen in the coming years. For instance, mean annual temperatures in Tanzania are predicted to increase by up to 2.7°C by 2060, and by close to 50% by 2090 (Irish Aid, 2018). Similarly, day and night temperatures are also expected to increase. Rainfall will become increasingly erratic both locally and regionally, with both floods and droughts growing in intensity and frequency. Smallholder farmers have the poorest access to resources such as land tenure, water resources, crop and livestock insurance, financial capital, and markets, and thus are the least risk-resilient. Women farmers in particular suffer systematic discrimination in terms of access to these resources. Women are also culturally expected to execute the most laborious agricultural tasks in addition to their household responsibilities of caregiving, preparing meals, and collecting fuel and water. Meanwhile, men tend to be responsible for tasks involving financial exchange, such as land acquisition, sourcing capital for production, purchasing and applying chemicals, and identifying buyers. This cultural norm is reinforced by the tenure system, which assigns land ownership almost exclusively to men. These factors make women the most vulnerable sub-group of smallholder farmers (Irish Aid, 2018). The national government, donor community, private sector, and development partners have invested in helping households prepare for such climate scenarios. A number of policies, strategies, programmes, and guidelines have been documented with the goal of boosting the adaptation capacity of vulnerable groups. Prominent among these are the National Agricultural Policy (NAP 2013), the National Climate Change Strategy (NCCS 2012), the National Adaptation Programme of Action (NAPA 2007), and the Climate-Smart Agriculture implementation guideline. Despite these efforts, several issues remain unaddressed due to a lack of coordination among relevant actors. The development of a local Climate-Smart Agriculture Profile can support the clarification of roles and crucial points of coordination to assist in this effort. This Kilolo District profile thus underscores the climate-smart agriculture (CSA) investments undertaken by farming households in the region. This profile is an output of the CSA/SuPER project on Upscaling CSA with small scale food producers, organized via the Village Saving and Lending Association (VSLA) Project, and implemented by Cooperative Assistance and Relief Everywhere (CARE International), the International Center for Tropical Agriculture (CIAT) (now part of the Alliance of Bioversity International and CIAT), Sokoine University of Agriculture, and Wageningen University and Research. Both qualitative and quantitative methods were used to gather the information herein, in accordance with the methodology employed by Mwongera et al. (2015). Secondary information was collected through an extensive literature review. Primary information was collected from interviews with agricultural experts, farmer focus group discussions, stakeholder workshops, and farmer interviews in Kilolo District. This profile is organized into six major sections based on the analytical steps of the study. The first section describes the contextual importance of agriculture to Kilolo livelihoods and households. The second describes historic and future climatic trends. The third section highlights farmers’ priority value chains. The fourth section addresses the challenges and cross-cutting issues in the sector. The fifth section details climate hazards experienced by farmers, as well as the current and proposed adaptation strategies. Finally, the sixth section outlines the policies related to CSA and the institutions that facilitate implementation of climate change initiatives

Outcome Evaluation of the work of the CGIAR Research Program on Water, Land and Ecosystems (WLE) on soil and water management in Ethiopia

In 2019, the CGIAR Research Program on Water, Land and Ecosystems (WLE) Leadership chose to evaluate WLE’s work in Ethiopia as one of its countries where it has had most success. The objectives of the evaluation are: To determine how and in what ways WLE contributed to the achievement of intended/unintended outcomes; Based on the findings of the evaluation, make recommendations of how WLE (and its partners) can become more effective in supporting soil and water management in Ethiopia; To serve as a participatory learning experience for WLE and its partners. This report describes the evaluation process, findings, conclusions and recommendations

Opportunities and constraints for improved vegetable production technology in tropical Asia

A description of the characteristics of vegetable production in tropical Asia is presented. The description is followed by a discussion of the opportunities and constraints of improved non-seed vegetable production technologies. Sowing of seeds and seedling emergence, transplant production, irrigation, mulching, fertiliser use, crop protection and weed control methods, protected cultivation and harvest date planning are discussed in relation to their use and impact. Conditions for successful introduction of new technologies and the role of outside actors are discussed. It is argued that in order to increase the success of adoption of improved technologies, farmers and public and private institutions should work together. With increasing prosperity, the demand for vegetables, especially in the expanding urban areas, will increase, leading to the intensification of production and higher profitability. With better profitability, the application of mulch, drip irrigation, fertigation and protected cultivation will become more common. With increasing production, harvest date planning as related to year-round city market demand, will become essential to improve profitability. It is recommended that, next to the development and introduction of improved production technologies, research and extension on vegetables in tropical Asia, should also focus on methods to improve harvest date planning and year-round suppl

Исследование достижений и перспектив развития технологических инновацийв области интеллектуальной сельскохозяйственной техники в Китае

Agricultural machinery is the key fi eld in modern scientifi c and technological innovation. In recent years, China has made great achievements in the development of high-performance intelligent agricultural machinery with cutting-edge technology, which promotes the effi cient use of agricultural resources and environment-friendly development, and supports 70 percent of China’s agricultural mechanization production. This paper mainly focus on the innovation and progress in the fi eld of intelligent agricultural equipment technology in China from the aspects of information perception and precision production monitoring technology, intelligent operation management technologies, power machinery, farmland operation machinery, intelligent harvesting technology, production technology and agricultural products processing equipment. the paper also summarizes that, in the future, green, intelligence and universality will become the main characteristics of the development of intelligent agricultural machinery technology, and cross integration, extension and expansion will become the main direction of technological innovation. At last by referring to the application basis and cutting-edge technology of China’s intelligent agricultural machinery industry, the innovation and development goals and research direction of future intelligent agricultural equipment, the scientifi c and technological innovation and industrial development trend in the fi eld of agricultural mechanization and intelligent application integration, this paper puts forward some suggestions on the research direction of future intelligent agricultural equipment.Сельскохозяйственная техника стала ключевой областью современных научных и технологических инноваций. В последние годы Китай добился больших успехов в разработке высокопроизводительной интеллектуальной сельскохозяйственной техники и применении передовых технологий, которые способствуют эффективному использованию сельскохозяйственных ресурсов и экологически безопасному развитию, а также обеспечивают 70 процентов производства в области механизации сельского хозяйства в Китае. В этой работе основное внимание уделяется инновациям и достижениям в области технологии интеллектуального сельскохозяйственного оборудования в Китае, а именно вопросам восприятия информации, технологии точного мониторинга производства, проблемам технологий интеллектуального управления операциями,  энергетического оборудования, машин для обработки сельскохозяйственных угодий, интеллектуальной технологии сбора урожая, технологий производства и оборудования для переработки сельхозпродукции. В статье также прогнозируется, что в будущем экологичность, интеллект и универсальность станут основными характеристиками развития технологий интеллектуальной сельскохозяйственной техники, а перекрестная интеграция, рост и расширение неотрывно связаны с технологическими инновациями. Наконец, на основе прикладного характера китайской интеллектуальной сельскохозяйственной техники и передовых технологий, учитывая цели инновационного развития и направления исследований будущего интеллектуального сельскохозяйственного оборудования, принимая во внимание научные и технологические инновации и тенденции промышленного развития в области механизации сельского хозяйства и возможности интеллектуальной интеграции, авторы выдвигают некоторые предложения в направлении исследований будущего интеллектуального сельскохозяйственного оборудования

Understanding the complexities of Building-Integrated Agriculture. Can food shape the future built environment?

Our food system is facing an unprecedented challenge: feeding a fast growing population without depleting precious resources like energy, soil, and water.Furthermore, the increasing urbanization has rapidly exacerbated the gap between farm to plate, leaving cities vulnerable to changes in the production and supply chain, as demonstrated by recent pandemics and wars. In this context, emerging technologies that allow plants to grow in absence of soil, permit to produce food in high densely built-up areas, bringing food production right were most consumers live. These initiatives enter within the so called Building-Integrated Agriculture (BIA), which is referred as the practice of locating greenhouses and soilless plant cultivation technologies on top and inside mixed-use buildings to exploit the synergies between the building environment and agriculture, involving resource recovery such as water, energy and nutrient flows. This paper aims at determining strategies, objectives, and best practices of BIA projects through the review of 21 case studies, to understand how a new advanced and future-oriented agriculture applied within the cities borders, can possibly shape the urban built environment and food systems of the future

IoT-Based Automatic Hydro-Organic Smart Farming System in Greenhouse with Solar Panels for Khok Nong Na

In this research, we propose IoT-Based Automatic Hydro-Organic Smart Farming System (AHOSFS) in Greenhouse with Solar Panels for Khok Nong Na, design and development with Internet of Things and solar energy. Data is collected from sensors for the following factors that affect plant growth: humidity, temperature, light, pH, EC, air quality, water temperature, and water level. The Firebase cloud-based application was used to collect all the data, and it enables farmers to monitor and control the system using their smartphones. The results showed AHOSFS can mix water and nutrient solution, which measure pH of 6.61 and EC of 1.23, control water level and mix nutrient solution in appropriate quantities. Green oak can thrive at pH levels between 6.0 and 7.0, EC levels between 1.1 and 1.7, and humidity level between 75 and 85. The sensor measured temperature value between 18 and 25 °C, which was adequate for the growth of green oak. Sensor also measured water temperature between 25 and 28 °C. The comparative value of cultivation in a hydro-organic system deployed between AB solution, organic fertilizer Formula 1 and Formula 2 found that organic fertilizer formula 1 has an effective and approximate AB solution at 81.64%. The fertilizer formula 2 has AB solution at 89.72%, which was more secure at a comparable level

A Smart Decision System for Digital Farming

[EN] New technologies have the potential to transform agriculture and to reduce environmental impact through a green revolution. Internet of Things (IoT)-based application development platforms have the potential to run farm management tools capable of monitoring real-time events when integrated into interactive innovation models for fertirrigation. Their capabilities must extend to flexible reconfiguration of programmed actions. IoT platforms require complex smart decision-making systems based on data-analysis and data mining of big data sets. In this paper, the advantages are demonstrated of a powerful tool that applies real-time decisions from data such as variable rate irrigation, and selected parameters from field and weather conditions. The field parameters, the index vegetation (estimated using aerial images), and the irrigation events, such as flow level, pressure level, and wind speed, are periodically sampled. Data is processed in a decision-making system based on learning prediction rules in conjunction with the Drools rule engine. The multimedia platform can be remotely controlled, and offers a smart farming open data network with shared restriction levels for information exchange oriented to farmers, the fertilizer provider, and agricultural technicians that should provide the farmer with added value in the form of better decision making or more efficient exploitation operations and management.This paper has been partially supported by the European Union through the ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIR and by the "Ministerio de Ciencia, Innovacion y Universidades" through the "Ayudas para la adquisicion de equipamiento cientifico-tecnico, Subprograma estatal de infraestructuras de investigacion y equipamiento cientifico-tecnico (plan Estatal i+d+i 2017-2020)" (project EQC2018-004988-P).Cambra-Baseca, C.; Sendra, S.; Lloret, J.; Tomás Gironés, J. (2019). A Smart Decision System for Digital Farming. Agronomy. 9(5):1-19. https://doi.org/10.3390/agronomy9050216S11995Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787-2805. doi:10.1016/j.comnet.2010.05.010Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A Survey. Mobile Networks and Applications, 19(2), 171-209. doi:10.1007/s11036-013-0489-0De Mauro, A., Greco, M., & Grimaldi, M. (2016). A formal definition of Big Data based on its essential features. Library Review, 65(3), 122-135. doi:10.1108/lr-06-2015-0061Haghverdi, A., Leib, B. G., Washington-Allen, R. A., Ayers, P. D., & Buschermohle, M. J. (2015). Perspectives on delineating management zones for variable rate irrigation. Computers and Electronics in Agriculture, 117, 154-167. doi:10.1016/j.compag.2015.06.019Vazquez, J. I., Ruiz-de-Garibay, J., Eguiluz, X., Doamo, I., Renteria, S., & Ayerbe, A. (2010). Communication architectures and experiences for web-connected physical Smart objects. 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops). doi:10.1109/percomw.2010.5470521Misra, S., Barthwal, R., & Obaidat, M. S. (2012). Community detection in an integrated Internet of Things and social network architecture. 2012 IEEE Global Communications Conference (GLOBECOM). doi:10.1109/glocom.2012.6503350Atzori, L., Iera, A., & Morabito, G. (2014). From «smart objects» to «social objects»: The next evolutionary step of the internet of things. IEEE Communications Magazine, 52(1), 97-105. doi:10.1109/mcom.2014.6710070Agrivi App http://www.agrivi.com/en/reApollo Project http://apollo-h2020.eu/Cambra, C., Sendra, S., Lloret, J., & Lacuesta, R. (2018). Smart System for Bicarbonate Control in Irrigation for Hydroponic Precision Farming. Sensors, 18(5), 1333. doi:10.3390/s18051333Ortiz, A. M., Hussein, D., Park, S., Han, S. N., & Crespi, N. (2014). The Cluster Between Internet of Things and Social Networks: Review and Research Challenges. IEEE Internet of Things Journal, 1(3), 206-215. doi:10.1109/jiot.2014.2318835Ji, Z., Ganchev, I., O’Droma, M., Zhao, L., & Zhang, X. (2014). A Cloud-Based Car Parking Middleware for IoT-Based Smart Cities: Design and Implementation. Sensors, 14(12), 22372-22393. doi:10.3390/s141222372Ning, H., & Wang, Z. (2011). Future Internet of Things Architecture: Like Mankind Neural System or Social Organization Framework? IEEE Communications Letters, 15(4), 461-463. doi:10.1109/lcomm.2011.022411.11012

Intelligent Agricultural Greenhouse Control System Based on Internet of Things and Machine Learning

This study endeavors to conceptualize and execute a sophisticated agricultural greenhouse control system grounded in the amalgamation of the Internet of Things (IoT) and machine learning. Through meticulous monitoring of intrinsic environmental parameters within the greenhouse and the integration of machine learning algorithms, the conditions within the greenhouse are aptly modulated. The envisaged outcome is an enhancement in crop growth efficiency and yield, accompanied by a reduction in resource wastage. In the backdrop of escalating global population figures and the escalating exigencies of climate change, agriculture confronts unprecedented challenges. Conventional agricultural paradigms have proven inadequate in addressing the imperatives of food safety and production efficiency. Against this backdrop, greenhouse agriculture emerges as a viable solution, proffering a controlled milieu for crop cultivation to augment yields, refine quality, and diminish reliance on natural resources [b1]. Nevertheless, greenhouse agriculture contends with a gamut of challenges. Traditional greenhouse management strategies, often grounded in experiential knowledge and predefined rules, lack targeted personalized regulation, thereby resulting in resource inefficiencies. The exigencies of real-time monitoring and precise control of the greenhouse's internal environment gain paramount importance with the burgeoning scale of agriculture. To redress this challenge, the study introduces IoT technology and machine learning algorithms into greenhouse agriculture, aspiring to institute an intelligent agricultural greenhouse control system conducive to augmenting the efficiency and sustainability of agricultural production

Review of intelligent sprinkler irrigation technologies for remote autonomous system

Changing of environmental conditions and shortage of water demands a system that can manage irrigation efficiently. Autonomous irrigation systems are developed to optimize water use for agricultural crops. In dry areas or in case of inadequate rainfall, irrigation becomes difficult. So, it needs to be automated for proper yield and handled remotely for farmer safety. The aim of this study is to review the needs of soil moisture sensors in irrigation, sensor technology and their applications in irrigation scheduling and, discussing prospects. The review further discusses the literature of sensors remotely communicating with self-propelled sprinkler irrigation systems, distributed wireless sensor networks, sensors and integrated data management schemes and autonomous sprinkler control options. On board and field-distributed sensors can collect data necessary for real-time irrigation management decisions and transmit the information directly or through wireless networks to the main control panel or base computer. Communication systems such as cell phones, satellite radios, and internet-based systems are also available allowing the operator to query the main control panel or base computer from any location at any time. Selection of the communication system for remote access depends on local and regional topography and cost. Traditional irrigation systems may provide unnecessary irrigation to one part of a field while leading to a lack of irrigation in other parts. New sensors or remotely sensing capabilities are required to collect real time data for crop growth status and other parameters pertaining to weather, crop and soil to support intelligent and efficient irrigation management systems for agricultural processes. Further development of wireless sensor applications in agriculture is also necessary for increasing efficiency, productivity and profitability of farming operations