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Farming is faced with the growing pressures of climate change, erratic weather patterns, and increasing disease outbreaks. Potato growers therefore need advanced solutions to optimise production and protect their crops. One of the most promising innovations in this space is the use of climatic data from strategically positioned weather stations combined with hyper-localised weather forecasts. This powerful combination helps producers improve irrigation scheduling and monitor disease pressure more effectively, driving better yields, cost efficiency, and sustainability.
Evolution of weather data
In recent years, advances in technology have made it possible to collect and analyse vast amounts of weather data with remarkable precision. Modern weather stations, equipped with sensors to measure various environmental factors, provide potato producers with crucial data on temperature, humidity, rainfall, solar radiation, wind speed, and evapotranspiration (ET0). These weather stations can be strategically placed within a broader potato-producing area to capture meso-climatic variations that affect crop health.
In addition to real-time data from weather stations, hyper-localised weather forecasts have emerged as a key tool for decision-making. Unlike general weather forecasts that cover broad regions, hyper-localised forecasts provide highly accurate predictions for small geographic areas, based on a feedback loop from a specific weather station.
These forecasts use data from a specific weather station in combination with satellite imagery, and advanced algorithms to predict conditions such as temperature fluctuations, precipitation, and humidity levels with much higher accuracy. When combined, weather station data and hyper-localised forecasts offer a comprehensive picture of current and future climatic conditions, empowering producers to make informed decisions regarding irrigation and disease management.
Irrigation scheduling
Water management is essential in potato farming, where under- and over-irrigation can have serious consequences. Under-irrigation can lead to drought stress, resulting in smaller tubers, lower yields, and reduced crop quality. Over-irrigation can cause waterlogging and leaching and increases the risk of diseases such as Pythium root rot. Striking the right balance is key to maximising water-use efficiency and crop productivity.
Climatic data from strategically placed weather stations can play a crucial role in optimising irrigation scheduling. By monitoring parameters such as soil moisture, temperature, and ET0 rates, producers can gain insights into how much water their crops need at different stages of growth. Evapotranspiration, in particular, is a valuable measure that combines water lost through evaporation from the soil surface and transpiration from the plants. Using this data, producers can determine the precise amount of water required to replenish the moisture lost, thus avoiding both water waste and stress on the plants.
However, relying solely on real-time weather station data can be limiting, especially when anticipating future changes in weather conditions. This is where hyper-localised weather forecasts come into play. These forecasts allow producers to anticipate upcoming rainfall events, heatwaves, or dry spells and adjust their irrigation schedules accordingly.
For instance, if a forecast predicts a significant rainfall event in the next 24 to 48 hours, a producer can delay irrigation, saving water and reducing costs. On the other hand, if a period of high temperatures and dry conditions is forecasted, the producers can increase irrigation in advance to prevent the crop from experiencing water stress.
By combining weather station data and hyper-localised forecasts, producers can fine-tune their irrigation practices in real-time and in anticipation of future conditions, ensuring that their crops receive the right amount of water at the right time.
Hyper-localised forecasting
Hyper-localised weather forecasts represent a significant advancement in precision agriculture, as they allow producers to make timely decisions based on highly accurate, location-specific weather predictions. These forecasts integrate data from various sources, including a specific weather station, radar systems, satellite imagery, and sophisticated modelling algorithms, to predict microclimatic conditions with remarkable detail. For potato growers, this means being able to anticipate changes in weather patterns that might not be captured by broader regional forecasts.
One of the key benefits of hyper-localised weather forecasts is the ability to predict frost events, which can have a devastating impact on potato crops. Frost can damage potato plants and reduce tuber quality, especially during the early growing stages. With access to precise frost predictions, producers can take proactive measures such as adjusting irrigation schedules to protect the crop. In some cases, irrigating the fields before a frost event can create a protective layer of water that prevents the plant tissues from freezing, a strategy known as ‘frost protection irrigation’.
Similarly, hyper-localised forecasts can help producers plan for heatwaves or extreme temperature fluctuations that might affect crop growth and yield. Armed with a very accurate seven-day weather forecast, producers can modify their irrigation strategies, be cognitive of the spray conditions for the next week as well as the disease pressure. This level of precision and foresight allows producers to maintain consistent crop health and optimise yields, even in the face of unpredictable weather
Disease pressure
Beyond irrigation, climatic data from weather stations and hyper-localised forecasts are invaluable for disease monitoring and prevention. Potato crops are highly susceptible to a range of diseases, many of which are triggered or exacerbated by specific weather conditions. For example, late blight, caused by the Phytophthora infestans pathogen, thrives in cool, wet conditions and can spread rapidly under the right circumstances. Early blight, bacterial wilt, and powdery scab are other diseases that are strongly influenced by environmental factors such as temperature and humidity.
Weather stations positioned strategically within potato production areas can track the precise conditions that favour the development of these diseases, including temperature, relative humidity, and leaf wetness duration. By continuously monitoring these factors, producers can detect when conditions are becoming favourable for disease outbreaks.
This real-time data can be fed into disease prediction models, which are often used to forecast the likelihood of infection based on specific thresholds. For instance, late blight models typically track periods of high humidity and moderate temperatures to predict when the risk of infection is highest.
A mean team
However, real-time data alone may not always be enough to anticipate a disease outbreak, particularly when weather patterns change rapidly. This is where hyper-localised weather forecasts prove to be a game-changer. These forecasts provide potato producers with a short- to medium-term (next seven days) outlook on conditions that could lead to disease development. For instance, if a forecast predicts a week of cool, wet weather, producers can take preemptive action by applying fungicides or other control measures before the disease has a chance to take hold.
Combining real-time weather data from stations with forecasted disease pressure allows producers to apply fungicides more strategically, reducing the need for unnecessary applications. This results in cost savings, lower environmental impact, and reduced risk of pesticide resistance. It also allows producers to focus on integrated pest management strategies, which combine cultural practices, biological controls, and targeted chemical applications to manage diseases more sustainably.
Placement of weather stations
To fully leverage the benefits of climatic data, the placement of weather stations is critical. Weather stations need to be strategically positioned within the potato production region to capture representative data that reflects the conditions experienced by the crop. In some cases, multiple weather stations may be required to account for variations in elevation, soil type, and proximity to water sources, which can create microclimates that differ significantly across the region.
The future of potato farming
As climate change continues to affect weather patterns and disease dynamics, the need for precise, data-driven farming practices will only grow. The combination of weather station data and hyper-localised weather forecasts provides potato producers with the tools they need to manage irrigation, predict disease outbreaks, and respond to changing conditions with agility and confidence.
With advancements in sensor technology, artificial intelligence, and machine learning, the integration of weather data into smart farming systems is expected to become even more sophisticated. Producers will be able to access predictive models, automated irrigation systems, and disease forecasting platforms that use real-time and forecasted data to optimise crop management at an unprecedented level of precision. – Emile Jordaan, general manager, METOS®SA.