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El Niño can Help Predict Cacao Harvests Up to 2 Years in Advance

When seasonal rains arrive late in Indonesia, farmers often take it as a sign that it is not worth investing in fertilizer for their crops. Sometimes they opt out of planting annual crops altogether. Generally, they're making the right decision, as a late start to the rainy season is usually associated with the state of El Niño Southern Oscillation (ENSO) and low rainfall in the coming months.

New research published in the Nature journal Scientific Reports shows that ENSO, the weather-shaping cycle of warming and cooling of the Pacific Ocean along the Equator, is a strong predictor of cacao harvests up to two years before a harvest.

This is potentially very good news for smallholder farmers, scientists, and the global chocolate industry. The ability to predict harvest sizes well in advance could shape on-farm investment decisions, improve tropical crop research programs, and reduce risk and uncertainty in the chocolate industry.

Researchers say that the same methods - which pair advanced machine learning with rigorous, short-term data collection on farmer practices and yields - can apply to other rain-dependent crops including coffee and olives.

"The key innovation in this research is that you can effectively substitute weather data with ENSO data," said Thomas Oberthür, a co-author and business developer at the African Plant Nutrition Institute (APNI) in Morocco. "Any crop that shares a production relationship with ENSO can be explored using this method."

About 80 percent of global cropland depends on direct rainfall (as opposed to irrigation), accounting for almost 60 percent of production. But rainfall data is sparse and highly variable in many of these regions, making it difficult for scientists, policymakers and farmers groups to adapt to the vagaries of the weather.

No weather data? No problem

For the study, researchers used a type of machine learning that did not require weather records for the Indonesian cacao farms that participated in the research.

Rather, they relied on data on fertilizer application, yields and farm type, which they plugged into a Bayesian Neural Network (BNN) and found that ENSO phases predicted 75 % of the variation in yields.

In other words, the sea-surface temperature of the Pacific accurately predicted cacao harvests in a large majority of cases for the farms in the study. In some cases, accurate predictions were possible 25 months before the harvest.

For the uninitiated, a model that can accurately predict 50% of yield variation is usually cause to celebrate. And such long-range predictive accuracy for crop yields crops is rare.

"What this allows us to do is superimpose different management practices - such as fertilization regimes - on farms and deduce, with a high level of confidence, those interventions that work," said James Cock, a co-author and emeritus researcher at the Alliance of Bioversity International and CIAT. "This is a whole paradigm shift toward operational research."

Cock, a plant physiologist, said that while randomized control trials (RCTs) are generally considered the gold standard in research, these are extremely costly and consequently often impossible to perform in developing tropical agricultural areas. The approach used here is much lower cost, requires no expensive collection of weather records and provides useful guidelines on how to better manage crops under variable weather.

Ross Chapman, a data analyst and the study's lead author, explained some of the key benefits of machine learning methods over conventional data analysis approaches:

"The BNN modeling differs from standard regression modeling because the algorithm takes input variables, such as sea-surface temperature and farm type, and then automatically 'learns' to recognize responses in other variables, such as crop yield," Chapman said. "The learning process uses the same fundamental process that the human mind learns to recognize objects and patterns from real-life experience. In contrast, standard models require manual supervision of different variables via human-generated equations."

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