Applying Climate Services to Transformative Adaptation in Agriculture

Despite the increasingly urgent need to significantly shift agricultural systems to achieve long term resilience, strategies to do so are rarely included in adaptation and development plans. A review of 196 Nationally Determined Contributions (NDCs), the commitments that countries made to climate action under the UNFCCC Paris Agreement, 9 National Adaptation Plans (NAPs), and 48 submissions to the UNFCCC Koronivia Joint Work on Agriculture (KJWA) platform shows that only 7 (or 2.7 percent) of these 253 documents describe applications of CS for adaptation planning that could be considered transformative; for example, by explicitly noting the use of long-term climate projections (see Appendix B). Yet an increasing number of agriculture system shifts will need targeted data from CS to phase in the significant, systemic changes that will be required to keep up with intensifying climate-change impacts.

To guide long-term climate resilience in agricultural production systems, CS must increase focus on decadal and longer-term climate-impact analysis within planning horizons out to around 2050. Existing CS have provided impressive benefits, including cost-benefit ratios ranging from 1:2 to 1:10 for investments in national meteorological services. Evidence suggests that using CS in agricultural decision-making resulted in gains of up to 66 percent in yields or income (Vaughan et al. 2017). To date, CS for agricultural adaptation have largely focused on daily weather and seasonal climate forecasts, which are critical for farmers to make day-to-day management decisions. However, planning for and implementing broader, longer-term interventions will require climate data that provide insights further into the future and should be combined with ongoing efforts to improve the shorter-term CS critical for daily and seasonal decision-making. Improved long-term (e.g., 2050) and decadal climate change projections must be coupled with information that illustrates which types of agricultural systems and the crop and livestock varieties within them can be expected to gain or lose productivity. Temperature and precipitation projections, for example, can be analyzed alongside crop models to gauge shifts in productivity over time.

To advance systemic change, CS must more effectively integrate information beyond agriculture, such as data on biological, environmental, economic, market, and social factors; and the use of climate information must be mainstreamed beyond planning for agriculture. For example, relocating agricultural systems, employing large-scale technology like irrigation, or shifting production type (e.g., cropping to aquaculture) will have significant implications on water supply and demand. Water is critical for all economic sectors, as well as environmental sustainability. Therefore, CS for agriculture must consider potentially competing water, land-use, and other demands from other sectors. CS that integrates information beyond agriculture and is used for more comprehensive planning can help to reduce inter-sectoral and inter-scalar competition and conflict. This will require investments in modeling, projections, and geospatial data that are downscaled, cross sectoral, and long-term, including economic feasibility and cost-benefit analysis, as well as coordination among many different government agencies and other groups that produce relevant data.

Enhanced CS need to be tailored to meet the needs of adaptation entities (e.g., policymakers, planners, funders, and practitioners) to assess the longer-term viability of existing agricultural production systems and their alternatives. For example, linking climate change projections with agro-climatological and other applied analyses would make it easier for key decision-makers to identify agricultural systems that may be approaching viability thresholds wherein climate impacts will become so severe that these systems will no longer be feasible, despite continued investment in system maintenance (although uncertainty will remain). Such analyses would enable decision-makers to better support the development of transformative adaptation pathways, that is, coordinated sequences of short- and medium-term actions or projects that can be phased in gradually to shift agricultural production systems, with sufficient flexibility to respond to new information (e.g., new information related to improved model skill or changes in global GHG emissions) (Carter et al. 2018).

In addition to enhanced CS, adaptation entities need additional support—tools, guidance, capacity and networks—to analyze and embed such information into plans, policies, and proposals. Although analytical systems that incorporate agricultural viability information are emerging, they are often overly complex for nonexpert users, which limits their use in adaptation planning processes. Government and other planners face additional challenges in putting decadal and multidecadal projections to use, such as issues of uncertainty, risk aversion, and the ability to justify investment based on projected risk (Singh et al. 2018). Agribusinesses are leading the way in using longer-term information to guide their investments. For example, coffee and cacao producers have mapped out which areas are likely to become more or less favorable for these crops and are investing or divesting accordingly. In contrast, less is being done to tailor CS to the needs of national-level agricultural planners and policymakers or those of bilateral and multilateral funding entities. The development of CS to support transformative adaptation must be driven by user groups, based on their needs and capacity and embedded within planning processes. Without access to high-quality, tailored CS on par with those being developed to meet the seasonal needs of farmers and by global agribusinesses, developing-country governments risk being left behind in active planning, and climate-induced inequalities will continue to widen (Islam and Winkel 2017).