Agriculture is one of the most climate-sensitive sectors, as it depends on favorable weather conditions for crop growth, development, and yield. However, agriculture is also facing increasing risks and uncertainties from climate change, which is altering the patterns, frequency, and intensity of temperature, precipitation, and extreme events across the globe.
Climate change can have significant impacts on agricultural production, food security, and rural livelihoods, by affecting the suitability, productivity, and stability of cropping systems, as well as the availability and quality of water, soil, and biodiversity resources.
To adapt and build resilience to climate change, farmers and agricultural stakeholders need to have access to timely, reliable, and actionable information on climate variability and change, at relevant spatial and temporal scales. Climate prediction tools are a set of methods, models, and products that can provide probabilistic and scenario-based information on future climate conditions, from sub-seasonal to multi-decadal timescales, and from local to global spatial scales.
Climate prediction tools can help farmers and agricultural stakeholders anticipate, prepare for, and manage climate risks and opportunities, by informing their decisions on crop selection, planting dates, input management, infrastructure planning, and policy support.
Principles and Types of Climate Prediction Tools
Climate prediction tools are based on the principles of climate science, which study the physical, chemical, and biological processes that govern the Earth's climate system, and their interactions with human activities and natural variability.
Climate prediction tools use a combination of observations, theory, and models to simulate and project the future state and evolution of the climate system, under different assumptions and scenarios of greenhouse gas emissions, land use change, and other forcing factors.
There are several types of climate prediction tools, which differ in their data sources, methodologies, time horizons, and output variables and formats.
Some of the main types of climate prediction tools are:
Seasonal Climate Forecasts
Seasonal climate forecasts are probabilistic predictions of the average weather conditions, such as temperature, precipitation, and wind, for the next 1-6 months, based on the initial state of the climate system and the expected evolution of the ocean-atmosphere interactions, such as the El Niño-Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the Indian Ocean Dipole (IOD).
Seasonal climate forecasts are typically produced by global or regional climate models, which simulate the physical processes of the climate system and their responses to the boundary conditions and forcings, such as the sea surface temperatures, the snow and ice cover, and the greenhouse gas concentrations.
Seasonal climate forecasts can provide valuable information for agricultural decision-making, especially for the planning and management of crop cycles, irrigation schedules, and input applications. For example, seasonal forecasts of above-normal rainfall can inform farmers to plant higher-yielding and water-sensitive crops, such as rice or maize, or to reduce fertilizer and pesticide use to avoid leaching and runoff.
Seasonal forecasts of below-normal rainfall can inform farmers to plant drought-tolerant and water-efficient crops, such as sorghum or millet, or to increase the irrigation frequency and amount to prevent water stress.
Some examples of seasonal climate forecast products and services for agriculture are:
- The International Research Institute for Climate and Society (IRI) at Columbia University produces a suite of global and regional seasonal climate forecasts, based on the ECHAM4.5 and CCM3.6 global climate models, and provides a map room and a data library for visualizing and downloading the forecast products, as well as a training and capacity building program for the users.
- The APEC Climate Center (APCC) in South Korea produces a multi-model ensemble seasonal climate forecast, based on the outputs of 17 global climate models from different institutions, and provides a web-based platform for disseminating and interpreting the forecast products, as well as a research and development program for improving the forecast skills and applications.
- The National Oceanic and Atmospheric Administration (NOAA) in the United States produces a set of operational seasonal climate outlooks, based on the Climate Forecast System (CFS) model and the North American Multi-Model Ensemble (NMME), and provides a user-friendly web portal and a mobile app for accessing and using the forecast products, as well as a network of regional climate centers and extension services for supporting the users.
Decadal Climate Predictions
Decadal climate predictions are scenario-based projections of the average climate conditions, such as temperature, precipitation, and sea level, for the next 10-30 years, based on the initial state of the climate system and the expected evolution of the natural and anthropogenic forcing factors, such as the solar variability, the volcanic eruptions, and the greenhouse gas and aerosol emissions.
Decadal climate predictions are typically produced by Earth system models, which simulate the coupled interactions of the atmosphere, ocean, land, and cryosphere, and their responses to the external forcings and internal variability.
Decadal climate predictions can provide useful information for long-term agricultural planning and investment, such as the development of new crop varieties, the expansion or relocation of agricultural areas, and the construction or upgrade of irrigation and drainage infrastructure.
For example, decadal predictions of increasing temperature and decreasing rainfall can inform the breeding and selection of heat-tolerant and drought-resistant crop genotypes, or the shifting of agricultural production to cooler and wetter regions.
Decadal predictions of sea level rise and coastal flooding can inform the protection or retreat of low-lying agricultural lands or the diversification of livelihoods and income sources for coastal communities.
Some examples of decadal climate prediction products and services for agriculture are:
- The UK Met Office produces a set of global and regional decadal climate predictions, based on the HadGEM3 Earth system model and the DePreSys3 prediction system, and provides a web portal and a data catalog for accessing and visualizing the prediction products, as well as a guidance and case studies for the users.
- The German Meteorological Service (DWD) produces a multi-model ensemble decadal climate prediction, based on the outputs of four Earth system models from different institutions, and provides a web-based platform and a climate data center for disseminating and analyzing the prediction products, as well as a research and education program for the users.
- The Japan Meteorological Agency (JMA) produces an operational decadal climate prediction, based on the MIROC5 Earth system model and the JMA-SGCPS prediction system, and provides a web portal and a data service for accessing and using the prediction products, as well as a capacity building and outreach program for the users.
Climate Change Projections
Climate change projections are scenario-based simulations of the long-term changes in the climate system, such as the global and regional patterns of temperature, precipitation, and sea level, for the next 50-100 years or beyond, based on different assumptions and pathways of future greenhouse gas emissions, land use change, and socioeconomic development.
Climate change projections are typically produced by Earth system models, which are coupled with integrated assessment models that simulate the interactions and feedback between the climate, the economy, and the society, and their responses to the policy and technology choices.
Climate change projections can provide essential information for long-term agricultural adaptation and mitigation planning, such as the assessment of climate change impacts and vulnerabilities, the identification of adaptation options and strategies, and the evaluation of mitigation potentials and trade-offs. For example, climate change projections of increasing frequency and intensity of droughts and floods can inform the development of drought-tolerant and flood-resistant crop varieties, the improvement of water storage and management infrastructure, and the strengthening of early warning and risk transfer systems.
Climate change projections of shifting agro-ecological zones and crop suitability can inform the diversification and intensification of cropping systems, the conservation and sustainable use of agrobiodiversity, and the promotion of climate-smart agriculture practices.
Some examples of climate change projection products and services for agriculture are:
- The Intergovernmental Panel on Climate Change (IPCC) produces a set of global and regional climate change projections, based on the outputs of multiple Earth system models and integrated assessment models, and provides a series of assessment reports and technical papers for synthesizing and communicating the projection results, as well as a data distribution center for accessing and visualizing the projection data.
- The Food and Agriculture Organization of the United Nations (FAO) produces a set of climate change impact assessments and adaptation strategies for agriculture, based on the IPCC climate change projections and the FAO crop and livestock models, and provides a web portal and a toolbox for sharing and applying the assessment results, as well as a capacity development and policy support program for the users.
- The CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS) produces a set of climate-smart agriculture profiles and investment plans for different countries and regions, based on the IPCC climate change projections and the CCAFS climate-smart agriculture prioritization framework, and provides a web platform and a learning alliance for co-developing and scaling the climate-smart agriculture solutions, as well as a research and innovation program for the users.
Applications and Benefits of Climate Prediction Tools in Agriculture
Climate prediction tools have numerous applications and benefits in agriculture, across different scales, regions, and farming systems. Some of the main applications and benefits of climate prediction tools in agriculture are:
Crop Management and Planning
Climate prediction tools can inform and optimize crop management and planning decisions, such as the selection of crop types, varieties, and rotations, the timing of planting, harvesting, and other field operations, and the application of inputs, such as water, fertilizers, and pesticides.
By using climate prediction tools, farmers can:
- Match the crop requirements with the expected climate conditions, and select the most suitable and productive crop types and varieties for their local context and farming system.
- Adjust the planting and harvesting dates to the predicted onset and cessation of the rainy season, and avoid the risks of crop failure or yield loss due to moisture stress or extreme events.
- Optimize the timing and amount of irrigation, fertilization, and pest control, based on the predicted rainfall, temperature, and humidity conditions, and reduce the costs and environmental impacts of input use.
- Plan the crop rotations and fallows, based on the predicted inter-annual and multi-year climate variability, and enhance soil fertility, water conservation, and pest and disease control.
For example, a study in Senegal found that using seasonal rainfall forecasts for millet management could increase the average yield by 10-25%, compared to the traditional calendar-based management, by informing the optimal planting dates and density, fertilizer application, and weeding and thinning operations, based on the predicted onset and amount of rainfall.
Livestock Management and Planning
Climate prediction tools can also inform and optimize livestock management and planning decisions, such as the selection of animal breeds and species, the timing of breeding, feeding, and health care, and the management of pastures, rangelands, and water resources.
By using climate prediction tools, livestock farmers can:
- Match the animal requirements with the expected climate conditions, and select the most adapted and productive animal breeds and species for their local context and farming system.
- Adjust the breeding and calving seasons to the predicted forage and water availability, and avoid the risks of animal stress, disease, or mortality due to feed and water scarcity or extreme events.
- Optimize the timing and amount of supplementary feeding, vaccination, and disease control, based on the predicted temperature, humidity, and disease outbreaks, and reduce the costs and risks of animal health problems.
- Plan the grazing and watering schedules and locations, based on the predicted rainfall and vegetation conditions, and enhance the sustainable use and conservation of pastures and rangelands.
For example, a study in Kenya found that using seasonal vegetation forecasts for cattle management could increase the average milk yield by 10-20%, compared to the traditional experience-based management, by informing the optimal grazing and watering locations, stocking rates, and supplementary feeding, based on the predicted forage and water availability and quality.
Water Management and Planning
Climate prediction tools can also inform and optimize water management and planning decisions, such as the allocation and distribution of water resources, the design and operation of irrigation and drainage systems, and the management of floods and droughts.
By using climate prediction tools, water managers and users can:
- Match the water supply and demand, and allocate the water resources among different sectors and users, based on the predicted rainfall, runoff, and evapotranspiration, and the economic, social, and environmental priorities and trade-offs.
- Design and operate the irrigation and drainage systems, based on the predicted crop water requirements, soil moisture, and water availability, and enhance the efficiency, productivity, and sustainability of water use in agriculture.
- Plan and implement the flood and drought management measures, such as the construction and maintenance of dams, reservoirs, and canals, the development and dissemination of early warning systems, and the provision of relief and recovery support, based on the predicted frequency, intensity, and duration of extreme events.
For example, a study in Australia found that using seasonal streamflow forecasts for irrigation scheduling could increase the average water productivity by 15-30%, compared to traditional fixed-schedule irrigation, by informing the optimal timing and amount of water application, based on the predicted crop water demand and water availability, and the economic value of the crop and water.
Risk Management and Insurance
Climate prediction tools can also inform and support risk management and insurance decisions, such as the assessment and pricing of climate risks, the design and implementation of risk transfer and sharing mechanisms, and the development and scaling of climate-smart agriculture practices.
By using climate prediction tools, risk managers and insurers can:
- Assess and quantify the frequency, severity, and impact of climate risks, such as droughts, floods, heat waves, and cold spells, on agricultural production, income, and livelihoods, based on the predicted climate hazards and the exposure and vulnerability of the agricultural systems and communities.
- Design and price the risk transfer and sharing mechanisms, such as weather index insurance, catastrophe bonds, and risk pools, based on the predicted probability and magnitude of the climate risks, and the cost and benefits of the risk management options.
- Promote and incentivize the adoption and scaling of climate-smart agriculture practices, such as soil and water conservation, agroforestry, and integrated pest management, as a means of reducing the exposure and vulnerability to climate risks, and enhancing the resilience and sustainability of the agricultural systems and livelihoods.
For example, a study in India found that using seasonal drought forecasts for weather index insurance could reduce the basis risk by 20-40%, compared to the traditional rainfall-based insurance, by informing the optimal design and pricing of the insurance contracts, based on the predicted crop yield losses and the willingness to pay off the farmers, and by providing targeted subsidies and extension services for the adoption of drought-resistant crops and practices.
Challenges and Opportunities for Climate Prediction Tools in Agriculture
Despite the numerous applications and benefits of climate prediction tools in agriculture, several challenges and limitations need to be addressed for their effective and equitable development, dissemination, and use.
Some of the main challenges and opportunities for climate prediction tools in agriculture are:
Scientific and Technical Challenges
Improving the accuracy, reliability, and resolution of climate predictions
Climate predictions are subject to uncertainties and errors, due to the complexity and variability of the climate system, the limitations and assumptions of the climate models, and the quality and availability of the input data and observations. Improving the accuracy, reliability, and resolution of climate predictions requires enhancing the understanding and representation of the physical processes and feedback in the climate system, the development and validation of the climate models and prediction systems, and the integration and assimilation of multiple data sources and observations.
Tailoring the climate predictions to the user needs and contexts
Climate predictions are often produced and communicated in technical and probabilistic terms, which may not be easily understandable and actionable for agricultural users and decision-makers. Tailoring the climate predictions to the user needs and contexts requires the co-design and co-production of the prediction products and services, the translation and interpretation of the prediction information into user-friendly and context-specific formats and languages, and the provision of training and support for the users to access, understand, and use the predictions.
Integrating the climate predictions with other data and models
Climate predictions are only one of the many factors that influence agricultural production and decision-making, along with other biophysical, socioeconomic, and institutional factors, such as the soil and water conditions, the market and policy incentives, and the farmer's knowledge and preferences. Integrating the climate predictions with other data and models, such as crop, livestock, and economic models, can provide a more comprehensive and realistic assessment of the climate impacts and risks, and inform the development and evaluation of the adaptation and mitigation options and strategies.
Socioeconomic and Institutional Challenges
Addressing the barriers and inequities in the access and use of climate predictions
Climate predictions are often not equally available and accessible to all agricultural users and stakeholders, due to the digital divide, the language and literacy barriers, and the power and resource imbalances. Addressing the barriers and inequities in the access and use of climate predictions requires the development and implementation of inclusive and equitable policies and programs, such as the provision of free and open access to the prediction data and tools, the translation and dissemination of the prediction information in local languages and media, and the empowerment and capacity building of the marginalized and vulnerable groups.
Ensuring the relevance and credibility of the climate predictions for the users
Climate predictions are often produced and disseminated by the scientific and technical communities, which may not have the same knowledge, values, and priorities as the agricultural users and stakeholders. Ensuring the relevance and credibility of the climate predictions for the users requires the engagement and participation of the users in the design, production, and evaluation of the prediction products and services, the integration of the local and indigenous knowledge and practices in the prediction process, and the establishment of trust and accountability between the producers and users of the predictions.
Building the institutional and financial sustainability of the climate prediction systems
Climate prediction systems require significant investments and capacities in the data, models, infrastructure, and human resources, which may not be available or affordable for many countries and organizations. Building the institutional and financial sustainability of the climate prediction systems requires the development and implementation of innovative and inclusive business models and partnerships, such as public-private partnerships, user-pays and value-added services, and regional and international cooperation and support.
Opportunities and Way Forward
Despite these challenges, there are also several opportunities and ways forward for advancing and scaling the development and use of climate prediction tools in agriculture.
Some of the main opportunities and recommendations are:
- Investing in the research and development of the next-generation climate prediction systems, which can provide more accurate, reliable, and relevant predictions at multiple scales and timescales, by leveraging the advances in the earth system sciences, the data sciences, and the computing and communication technologies, such as the high-resolution and coupled models, the machine learning and artificial intelligence techniques, and the cloud computing and mobile applications.
- Promoting the co-design and co-production of climate prediction products and services, which can ensure the relevance, credibility, and usability of the predictions for the agricultural users and stakeholders, by engaging them in the design, production, and evaluation of the prediction systems, by integrating their knowledge, needs, and feedback in the prediction process, and by building their capacity and ownership in the use and application of the predictions.
- Strengthening the institutional and policy support for the climate prediction systems can enable the sustainable and equitable development and use of the predictions in agriculture, by providing the legal, financial, and technical resources and incentives for the prediction producers and users, by fostering the collaboration and coordination among the different sectors and stakeholders, and by mainstreaming the climate predictions in the national and regional agricultural policies and plans.
- Enhancing the communication and dissemination of climate prediction information can increase the awareness, understanding, and use of the predictions by agricultural users and decision-makers, by using the appropriate and effective channels and formats, such as the radio, television, mobile phones, and social media, by translating the predictions into the local languages and contexts, and by providing the training and support for the users to interpret and apply the predictions.
Conclusion
Climate prediction tools are essential for enhancing the resilience and sustainability of agriculture in a changing climate, by providing information and insights on future climate conditions and risks and informing the adaptation and mitigation decisions and actions.
Climate prediction tools, such as seasonal forecasts, decadal predictions, and climate change projections, can help agricultural users and stakeholders to anticipate, prepare for, and respond to climate variability and change, and to optimize crop and livestock management, water and land use planning, and the risk and insurance management.
However, the development and use of climate prediction tools in agriculture also face several scientific, technical, socioeconomic, and institutional challenges, which need to be addressed through investment in research and development, the co-design and co-production with users, the institutional and policy support, and the communication and dissemination of the prediction information.
By advancing and scaling the climate prediction tools in agriculture, we can contribute to the achievement of the Sustainable Development Goals, particularly SDG 2 on zero hunger, SDG 13 on climate action, SDG 15 on life on land, and the realization of the Paris Agreement on climate change.
Climate prediction tools are not a panacea or a silver bullet for the complex and multifaceted challenges of agriculture and climate change, but they are a valuable and indispensable component of the broader efforts and strategies for building the resilience and sustainability of the agricultural systems and communities.
The successful development and use of climate prediction tools in agriculture require a holistic and integrated approach that considers the biophysical, socioeconomic, and institutional dimensions of the agricultural systems and the climate risks, and that engages and empowers the diverse stakeholders and users, from the farmers to the policymakers to the scientists.
As we face the increasing impacts and uncertainties of climate change on agriculture and food security, climate prediction tools offer a promising and transformative pathway for enhancing the adaptive capacity and resilience of the agricultural sector and rural livelihoods.
By harnessing the power and potential of climate prediction tools, while also addressing their challenges and limitations, we can create a more climate-smart and sustainable agriculture that can feed the world and protect the planet, now and in the future.