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.

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Brown rot is a common fungal disease in the Southeastern United States that affects stone fruits like peaches, cherries, and plums. Monilinia fructicola is a tan, fuzzy fungus that grows on tree flowers, twigs, and fruit, and is the most common cause of brown rot. Once brown rot infects a tree, the damage is irreversible, causing significant crop losses in commercial orchards. Farmers try to control the spread of M. fructicola by applying chemical sprays, called fungicides, to fruit trees in the spring.

In M. fructicola, a biosynthesis gene named MfCYP51 encodes a major protein critical to the fungi’s survival. Certain fungicides, called demethylation inhibitor (DMI) fungicides, stop M. fructicola from growing by targeting this protein. Over the last 20 years, DMI fungicides have become less effective against brown rot as M. fructicola becomes more resistant. 

Living microorganisms and their metabolites can control fungal diseases as an alternative to chemical fungicides, known as biofungicides. Many bacteria produce compounds like hydrogen cyanide and pyrrolnitrin that damage fungal cells. However, scientists have not found a biofungicide suitable for commercial production.​ A group of researchers at Clemson University in the USA and Huazhong Agricultural University in China set out to test whether the soil bacteria Pseudomonas chlororaphis and Bacillus subtilis could be effective biofungicides against brown rot. 

The researchers performed an incubation experiment to analyze how the fungal cells make their critical protein from the MfCYP51 gene, a process called gene expression, under different fungicide treatments. They used 3 M. fructicola strains sensitive to traditional DMI fungicides and 3 resistant strains. They tested these with 5 different fungicide treatments: a DMI fungicide, a mixture of P. chlororaphis metabolites, a mixture of living B. subtilis cells, a DMI fungicide + P. chlororaphis metabolites, and a DMI fungicide +  B. subtilis cells. They also set up a control treatment with sterile water instead of fungicide.  

After 6 hours, the researchers extracted the molecule that measures gene expression, called RNA, from the fungi. They found that both the P. chlororaphis treatment and the DMI fungicide + P. chlororaphis treatment substantially reduced MfCYP51 expression in both the sensitive and resistant isolates compared to the controls. In contrast, the DMI fungicide, B. subtilis, and DMI fungicide + B. subtilis treatments increased MfCYP51 expression in the resistant isolates. The researchers concluded that P. chlororaphis has a unique ability to reduce gene expression, unlike other biofungicides.

​To further investigate how the biofungicide treatments work, the team tested whether P. chlororaphis and B. subtilis produced the anti-fungal metabolite pyrrolnitrin. They added 1 milliliter of each treatment to a machine that separates and identifies liquid compounds using high pressure, called a high-performance liquid chromatograph. They found that the P. chlororaphis treatment contained pyrrolnitrin, so they hypothesized that it was responsible for reducing gene expression.

Additionally, the researchers tested their 5 treatments on brown rot in fruit. For each treatment, they took 10 Gala apples, washed and sterilized their surfaces, and sprayed them with the fungicides. After 24 hours, they poked holes in the apples with toothpicks and added 20 microliters of liquid containing M. fructicola cells to the poked holes. They placed the apples in a humid chamber for 5 days and measured the number and size of brown rot spots that developed on their surfaces. 

Despite decreasing MfCYP51 expression, ​the researchers found that P. chlororaphis treatment alone didn’t reduce brown rot disease compared to the control, but the DMI fungicide and B. subtilis treatments did. The mixed treatments of DMI fungicide + P. chlororaphis and DMI fungicide + B. subtilis both reduced brown rot disease compared to the control, but only the DMI fungicide + P. chlororaphis treatment prevented it. 

​The researchers concluded that although biofungicides may not be fully effective on their own, commercializing them could allow farmers to use less DMI fungicide. They explained that scaling back the use of DMI fungicides would slow M. fructicola resistance. Finally, they proposed that future researchers test biofungicide mixtures containing pyrrolnitrin in the field to observe real-world effects on stone fruit trees.

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