In the Bolivian Altiplano, fewer Aymara farmers are relying on local weather knowledge to schedule farming tasks, like choosing the best dates to plant crops and predicting a frost or drought. At the same time, there is growing doubt about whether these traditional indicators remain relevant today in the context of rapid social, economic, and climatic changes.
Farmers are struggling because the National Weather Service of Bolivia (SEHNAMI) fails to provide useful weather forecasts due to the geographical complexity of the Andes and a scarcity of weather stations. In addition, many farmers can’t access this information because SEHNAMI lacks local outreach programs to deliver reports directly to them. In a region with a weak technical forecasting system, local knowledge is a vital resource, but its use is declining.
Scientists in the past focused on preserving local weather knowledge or discussing how it might help communities adapt to climate change. However, very few researchers have scientifically validated local weather indicators, since doing so requires many years of historical meteorological data to establish statistical “skill” in local forecasting.
Faced with this gap, researchers in the US and Bolivia recently set out to validate indigenous indicators in 2 Bolivian municipalities: Umala in the Central Altiplano and Ancoraimes in the Northern Altiplano. To do this, they paired each traditional weather sign with a measurable variable, like temperature, that could approximate it. This approach allowed them to directly compare traditional observations with scientific data.
The researchers wanted to determine whether traditional weather knowledge is still accurate and relevant under current conditions, since validating these methods would strengthen local knowledge and benefit the schools and policies that promote it. To test this, the researchers worked with 6 Aymara communities. First, they conducted workshops with local leaders and experts to compile a set of weather-forecast indicators. Then, they surveyed 95 farmers to rank the reliability of these indicators.
Through discussions with these communities, the team identified 13 weather indicators in the Central region and 20 in the Northern region. These indicators included specific plants, animals, and physical factors like constellations or winds that the farmers used to decide when and where to plant crops, and to predict weather conditions for the next season. The researchers focused on 2 key indicators that the farmers ranked as most reliable: the presence or absence of moisture under rocks on the Fiesta de San Juan on June 24th, and the flowering patterns of specific plants, called Thola and Sank’ayu. The farmers used the San Juan indicator to predict whether the upcoming year would be rainy or dry, and the plants to determine the most favorable dates for planting to avoid agricultural risks like frosts.
Finally, the researchers compared forecasts based on these 2 signs with over 30 years of meteorological data from nearby weather stations. In both cases, they used the minimum daily temperature as the measurable way to represent these local signs scientifically. Their results showed that the minimum daily temperature on the Fiesta de San Juan could explain more than half (55.5%) of the changes in rainfall during the growing season. They also confirmed that severe frosts that “burned” Thola and Sank’ayuthe flowers were followed by periods without rain, validating the farmers’ strategy of delayed planting.
The researchers concluded that local weather indicators used by Aymara farmers are most reliable for seasonal planning, especially in predicting the start of the rainy season. They stated that this result helps legitimize traditional knowledge and empower the community. For example, these communities now hold regular meetings to discuss forecasts and share this information with their neighbors via WhatsApp groups.
However, the team acknowledged that local knowledge was limited to short-term forecasting, as it only provided warnings a few hours in advance. Farmers need alerts several days in advance to effectively reduce their vulnerability to events such as frosts or hail. For this reason, the researchers suggested that the next step will be to co-produce forecasts by combining meteorological science with traditional knowledge.
