Using Climate Forecasts in Supply Chains
By Paulina Concha 13 January, 2015
Columbia Water Center's Concha on the art of climate forecasting & how it can mitigate supply chain risk
Climate forecasts can facilitate risk reduction for corporates & farmers
Companies that depend on agricultural commodities are strongly impacted by extreme weather. As an example, an abnormally wet summer in the UK in 2012 caused the nation’s total potato production to fall to the lowest level since 1976 (UK Potato Council, 2013). On the other hand, the cause of yield reduction in 1976 was an extremely dry and hot season.
When climate events like extreme heat or wetness occur losses are propagated through the supply chain…
Companies dependent on agricultural commodities are strongly impacted
When such events occur, losses are propagated through the supply chain; costs increase and supply chain strategies have to be modified, as companies need to seek other supply sources, in addition to the impact on the livelihood of farmers.
Early warning of extreme weather events using climate forecasts can facilitate risk reduction for corporations and for partnering farmers. If unfavorable conditions are predicted, short-term investments in on-site measures can increase the resilience of farmers, and water management for irrigation can be better planned, facilitating a more robust supply chain. Corporations involved in contract farming or partnerships with farmers can provide the assistance to mitigate the risks through appropriate planning and support.
The idea of using climate forecasts as a risk mitigation strategy first came about in the last decade. The 1997–1998 El Niño event, led to losses in the U.S. agriculture sector estimated at $1.5-$1.7bn, while the 1998-1999 La Niña event led to losses ranging from $2.2 to $6.5bn (Adams et al 1999). Still, farmers and corporations have been reluctant to incorporate climate predictions in their operations due to the uncertainty of the forecasts (Hartman, 2002).
But uncertainty & scalability of forecasts hinder their adoption
A problem is that climate forecasts have not been available at a relevant scale (temporal and spatial) to quantify and address risks seen by users.
Further, most seasonal climate forecasts typically provide only coarse probabilities of total seasonal rainfall categories, e.g., below normal, normal or above normal (see chart).
From such information it is not easy to assess the potential impacts on crop yield or crop water requirements.
Columbia Water Center – the art of climate forecasting
To address these issues, researchers at the Columbia Water Center (CWC) have developed different products that inform sourcing and irrigation plans based on forecasts of yield-related climate indices at the local scale.
These forecasts are issued at least a month prior to the start of the planting season to facilitate decisions to prevent supply chain issues.
The Columbia Water Center has developed products that inform sourcing & irrigation plans based on forecasts of yield-related climate indices at the local scale
The lead-time and the skill of these forecasts varies from region to region and from season to season. Weather in some regions is strongly influenced by larger scale atmospheric phenomena occurring over long periods of time. In such cases forecasts can be quite reliable (Nissan, H, 2014).
On the other hand, local processes such as convection influence rain in other regions, making long-term predictability in those areas more challenging.
To communicate the uncertainty of the models, the CWC provides a probabilistic forecast of the variables of interest along with the measure of the skill of the model (usually Rank Probability Skill Score). We also monitor climate forecasts from the leading global climate centers. These are typically based on physical models of the climate system, and focus on global prediction of seasonal rainfall totals and average temperature.
The model’s certainty depends largely on how well the current conditions track forward into the future, and the results are available for relatively large regions – e.g., the computations are done for areas of 250km by 250km, and the reliability increases as several such areas are averaged. Researchers at the CWC monitor the skill and indications of these models, and can use the resulting forecasts as predictors in regions where they have some skill.
A combination of statistical models from CWC & physical models from other centers can in some cases reduce the uncertainty of predictions
Climate forecasts from other centers are also used as an extra validation step, comparing them with the seasonal outlooks developed at the CWC. A combination of statistical models from the CWC and physical models from other centers help assess, and in some cases reduce, the uncertainty of predictions.
The CWC monitors the weather during the season as well as weekly forecasts from other centers for the validation and testing of their pre-season forecasts, and shares these with clients on a continuous basis.
The pilot for this work started in 2012, when PepsiCo, Inc. partnered with the CWC to assess climate/water risk impact on potato production in different regions of importance for PepsiCo’s Frito Lay business. The initial test cases were in India, the United States and the United Kingdom. The project is now expanding to other regions.
CWC developed customized forecasts of variables (eg. length of wet and dry cold spells) that impact potato yield for PepsiCo’s Frito Lay business …
… when the forecasts reach an acceptable level of uncertainty, they can be incorporated into the company’s supply chain strategy
The CWC has developed customized forecasts of variables that impact crop yield in the regions of study. These variables include seasonal precipitation totals, length of wet, dry and, cold spells, number of days with extreme rainfall, water deficit based on crop requirements, growing degree-days, and number of dry days.
Historical data on daily precipitation, temperature and other climate variables, as well as monthly data on Sea Surface Temperatures and atmospheric circulation variables from global climate centers and their updated models are used to develop the forecasts at a high level of granularity.
The models are continuously being improved, as the understanding of the variables and timing that affect production advances, and better modeling techniques are applied.
The ultimate aim is that forecasting capability will one day cover all of PepsiCo’s agricultural supply areas to provide a global view of their sourcing regions. Once the forecasts achieve an acceptable level of uncertainty, the next step is to incorporate their outcomes into the supply chain strategy. Some regions will likely retain a higher level of uncertainty, but the forecasts can still be used to develop contingency plans at the company and farm levels by accepting different levels of risk.
Climate forecasts to play bigger role in corporates as risk increases
Besides planning for supply, forecasts can be used in storage and distribution strategies (Everingham et al, 2012). Optimization models for supply chain investment and sourcing that address the uncertainty in forecasts are also being developed to facilitate decision making at the corporate and farmer level.
Climate science is advancing & prediction uncertainties should fall…
Widespread adoption of forecasts can prevent or mitigate loses caused by climate variability: US$32 billion of uninsured loses for the 2011 floods in Thailand & US$15.2 billion for 2013 floods in Germany
This joint venture shows how climate forecasts can be used for decision-making purposes not only within farms but also at a corporate level. As supply chains are interconnected regionally and globally, and the expectations of diverse stakeholders are increasing, more companies are required to have a climate risk assessment plan.
Recent cases of extreme weather events such as the 2011 floods in Thailand ($32 billion of uninsured loses, MunichRE, 2014) and the 2013 floods in Germany ($15.2 billion of uninsured loses, MunichRE 2014) have demonstrated the relevance of forecasts to prevent or mitigate loses caused by climate variability.
Climate science is advancing and scientists are gaining a deeper understanding of how to model atmospheric physics; these advances will lead to a reduction of prediction uncertainties and encourage the widespread adoption of model forecasts in risk assessment plans for different sectors.
Adams M. Richard, Chen Chi Chung, The economic consequences of ENSO events for agriculture, Clim Res, Vol 13: 165–172, 1999 https://www.int-res.com/articles/cr/13/c013p165.pdf
Everingham Y.L., Muchow R.C. Enhanced risk management and decision-making capability across the sugarcane industry value chain based on seasonal climate forecasts, Agricultural Systems 74 (2002) 459–477
Hartman, H, Pagano C. T. (2002), Evaluating seasonal climate Forecasts from user perspectives, American Meteorological Society, May 2002, pp. 683 ftp://18.104.22.168/downloads/factpub/wsf/Hartman_et_al_Climate_Research_2002.pdf
Meza J.F., Hansen J.W., (2008) Economic value of seasonal climate forecasts for agriculture : Review of Ex-ante assessments and recommendations for future research, American Meteorological Society, Vol. 47, pp:1269
Nissan, Hannah, The challenge of seasonal weather prediction, Climate at Imperial, Imperial College London, April 14th, 2014, https://wwwf.imperial.ac.uk/blog/climate-at-imperial/2014/04/14/the-challenge-of-seasonal-weather-prediction/ last accessed on October 24th, 2014
UK Potato Council (2013) Agriculture and Horticulture Development Board (AHDB), Market Intelligence 2013-2014, Potato Council, https://www.potato.org.uk/sites/default/files/%5Bcurrent-page%3Aarg%3A%3F%5D/GB%20Potatoes%20Market%20Intelligence%202013-14.pdf , last accessed on Oct 24th 2014
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