Climate-Smart Agriculture is a sustainability-focused approach which increases agricultural productivity, while also building resilience to climate change and, where possible, reducing or removing greenhouse gas emissions. Dr Trung Hieu Tran, Lecturer in Data Analytics, Lead of Data-driven Innovation Community of Practice at Cranfield’s Centre for Competitive Creative Design (C4D), shares his insights into what Climate-Smart Agriculture is and how it can make farming economies more resilient and less vulnerable to climate change.
In developing countries, the agriculture sector has rapidly developed to become a major contributor to their national economy, with a majority of the population working within the sector. However, governments have understandably raised concerns regarding the long-term sustainability of agriculture in the light of the impacts of extreme events linked to climate change, such as flooding and drought, and the subsequent influences of these events on crop quality and productivity.
Although some policies have been proposed to promote more resilient and sustainable agricultural development, there remains a raft of major agricultural transformation challenges facing this sector. Climate-Smart Agriculture (CSA) has recently been proposed as an efficient approach, which can help to guide the actions needed to transform and reorient agricultural systems to effectively support development and ensure food security under the impacts of climate change.
What is CSA and what are the benefits?
The Food and Agricultural Organisation of the United Nations (FAO) defines CSA as “agriculture that sustainably increases agricultural productivity and incomes, adapts and builds resilience to climate change, and reduces and/or removes greenhouse gas emissions, where possible”. In other words, CSA is an approach to develop agricultural strategies for resilient and sustainable food security under climate change. This approach can thereby provide the solutions to support stakeholders at local, national and international levels to determine agricultural strategies suitable to their conditions.
In line with the FAO’s vision for Sustainable Food and Agriculture, CSA supports the FAO’s goal to make agriculture, forestry and fisheries more productive and more sustainable by contributing to the achievement of several of the Sustainable Development Goals (SDGs):
- SDG1: End hunger, achieve food security and promote sustainable agriculture.
- SDG5: Achieve gender equality by creating more jobs for women.
- SDG10: Reduce incoming inequality for women.
- SDG12: Ensure resilient and sustainable production patterns.
- SDG13: Reduce the impact of climate change on agriculture.
- SDG17: Strengthen the global partnership for sustainable agricultural development.
CSA models are used to underpin modern farm management systems that monitor crop development, environmental variables, and farmer practices. The farming management system is based on an integration of satellite/drone images, sensor technologies, mobile applications, positioning technology and the Internet of Things. Big data analytics, machine learning and artificial intelligence techniques are used in combination with cloud located databases to guide the accurate and efficient implementation of agricultural robotics and automation. This system is referred to as the “SMART” platform in which:
S = Satellite/drone images data for plant health
M = Meteorological data for crop environment
A = Application data for farmer practices
R = Robotics or machineries for precise implementing practices and,
T = Terminal for data visualization and analytics
Figure 1: A schematic diagram for the data flows in the SMART platform .
Thailand has been one of the pioneering countries to implement the SMART platform, with a pilot project in the central part of the country. The first phase (2017-2019) of the implementation collected a data set on plant health, microclimate and farmer practices in selected regions, and visualised them on a dashboard to monitor plant heath, temperature, humidity, rainfall, plant species, varieties, planting time and harvest time. The information is being collected and updated continuously to create a large data set to support advanced analysis, prediction, and forecasting. Planners can then utilise the information for agricultural planning and management via a decision support system (DSS). This system helps planners to optimise the operational performance of robotics and automated systems for operations such as rice drilling, rice seeding and pesticide spraying using drones. The impacts of climate change – such as through flooding and drought - on crop quality and productivity in the agricultural sector, will therefore be significantly mitigated.
The future of CSA in South-East Asian countries
Mr Chu Van Chuong, Deputy Director General at the Vietnamese Ministry of Agriculture, has stated that: “in Vietnam, climate change affects 30% of the land. Typhoons and salt water intrusion reduce crop production. For example, in the Mekong Delta, an area which produces about 90% of Vietnamese exported paddy rice, severe salt water intrusion and soil erosion affect the livelihood of the people. That’s why it is important to develop a climate change strategy to reduce the coming economic shock due to crop losses.” 
With Thailand’s preliminary success in the implementation of the SMART platform, it can be considered as a prototype for other developing countries such as Vietnam, Myanmar and Cambodia. These countries are in the global top 10 of rice exporting countries. Rice exporting revenue has largely contributed to their national GDP and CSA can play a huge role in making their farming system and farming economy more resilient and less vulnerable to climate change.
CSA research at Cranfield
Here at Cranfield’s Centre for Creative, Competitive Design (C4D), I have developed international collaborative research projects to support developing countries in solving global challenges including climate challenge impacts on the agricultural sector. I have also recently applied for funding to run a workshop in Hue city, Vietnam next year entitled “Towards Climate-Smart Agriculture in SEA Countries”. The workshop will bring together researchers from the UK and several South East Asian countries, such as Vietnam, Thailand, Myanmar, Laos, Cambodia, to address climate challenges in the sector and enhance economic development, social welfare and environmental quality.
I’m also leading Cranfield’s researchers from Operational Research/Management Science (i.e. big data analytics, prediction, optimisation and simulation, multi-criteria decision-making) in the collaboration with other experts in Robotics and Automation, Agrifood Systems, Carbon, Climate and Risk to develop and work on the international collaborative projects. A novel integrated DSS will be designed to collect, analyse and display data; model, simulate and predict scenarios; train and guide robotics and automatic machineries for efficient agricultural planning, operations and management.
 Sreewongchai, T. and Nakasathien, S. (2020). Smart platform for precision agriculture in Thailand. FFTC Agricultural Policy Platform. URL:
 Sarzynski T. (2019). Climate smart agriculture in Vietnam. A glimpse at Vietnam agricultural-climate change policy.
Dr Trung Hieu Tran, Lecturer in Data Analytics, Lead of Data-driven Innovation Community of Practice, Cranfield’s Centre for Competitive Creative Design (C4D).