Cocoa; future yields across West Africa

Seventy percent of the world cocoa production is produced by small holders in West Africa. However, yields are low and at risk from climate change, pests and diseases and many cocoa farming families live around or below the international poverty line. Major changes are needed to develop a sustainable supply chain.
Being able to accurately predict cocoa yields and production will be one such change. Currently, accurate production forecasts are only available to a handful of large multinational cocoa companies and brokers, who run their proprietary forecasting programmes, based on physical monitoring of hundreds of cocoa farms. All other actors in the cocoa industry lack a reliable source of information about the expected harvest. However, such forecasts are essential to support planning and adaptation across the supply chain. They are also essential to price formation, which is determined in futures markets, which rely on participants' expectations of future harvests. Currently, information is asymmetrical in the market, favouring the few big players.
Here we propose to use a demonstrated technology to generate accurate forecasting models at a fraction of the cost of the proprietary systems, making them affordable for a broad audience in the cocoa industry and hence contributing to the democratisation of the cocoa sector. We combine a range of data sources now available over long periods (20-40 years) with machine learning and models to generate a robust forecast tool. We now have time series mapping of critical determinants of yield, including rainfall, leaf area index, surface soil moisture, soil physical properties. We also have regional and national yield data. There is strong evidence of climate (soil moisture) impacts on yield, but little information on the critical thresholds in drought stress effects. Here we use process modelling to link plant production to soil moisture through the rooting zone. We calibrate our model at site scale to couple plant-soil processes, and then calibrate the model at regional and national scale to couple yield to plant-soil indicators. The outcome is a robust tool for generating forecasts of yield through the year, with clear confidence intervals. This information provides the novel input for the cocoa value chain.
The output from this research will form the scientific basis on which commercial providers can offer products and services to end-users in the cocoa industry, as demonstrated by two project partners. Once proven for cocoa, the approach can be replicated in a range of other crops, from grains to tropical commodities to vegetable oils.

Cocoa industry
Currently, less than 1% of actors in the cocoa industry (not counting farmers and producer cooperatives) have access to accurate production forecasting systems. With our technology, we estimate that 50% or more of the actors in the industry will be able to afford accurate forecasts.
This will
-Level the playing field in the cocoa market between small and large actors
-Reduce the scope for commodity speculation, which acerbates price swings and uncertainty in the market
-Overall stabilize world cocoa prices
-Increase certainty in the cocoa supply chain and reduce hedging costs, benefitting all actors along the chain, including cocoa farmers.

The ability to predict cocoa yields in a changing climate will be beneficial to NGOs and development programmes working with cocoa farmers as well as to the wider industry.

Other sectors
The cocoa model will serve as an entry point for other industries: Once proven effective, the concept can be replicated for other crops with minor adjustments to the technology. Cocoa is a small specialty commodity compared to other interesting industries like when, barley, maize, soy, coffee, cotton, rubber or palm oil.

Commercialising companies
The results from this project will be published. Companies developing services to the cocoa and other industries will be able to package the technology into products and services that best serve their clients. We expect that there may be competition for the best solution as well as solutions that address different segments of the end-user market. It is expected that this will lead to an optimised landscape of commercial products and services based on the technology and thereby ensure the most efficient utilisation of the invested NERC resources.