A new study by researchers at Oklahoma State University’s Department of Plant and Soil Sciences may well improve the predictability of seasonal droughts and provide a better way for farmers to determine when drought conditions are likely to occur.
The study, which began as a student-led class research project, was published Jan. 29 in Agronomy Journal.
Researchers Guilherme M. Torres, Romulo P. Lollato and Tyson E. Ochsner say drought can cause yearly loses of $8 million in the United States and can severely limit crop growth throughout the year in which they occur but believe a reliable calendar of seasonal drought patterns can be created that could help farmers optimize crop productions by avoiding periods of severe drought.
The study underscores the problem of current drought prediction practices, suggesting that current methods that estimate the probability of agricultural drought using atmospheric data can be widely applied but have not been compared with actual drought occurrence indicated by soil moisture measurements. The researchers’ objective was to develop a drought probability assessment method using long-term measurements of soil water deficits and to compare the resulting probabilities with those of an existing method based on atmospheric water deficits.
Tyson Ochsner, lead author of the study, says soil moisture needs to be factored into drought prediction methodologies to get a better picture of how it affects seasonal conditions.
“Soil moisture can provide an important buffer against short-term precipitation deficits,” he says, and calculating soil moisture for prediction models can greatly increase accurate drought forecasting.
Defining methods and models
Ochsner called the prediction method utilizing only atmospheric water deficit the AWD and refers to the method that factors in the soil water deficit as the SWD.
Fifteen years of daily precipitation, air temperature, and soil moisture measurements for eight locations across Oklahoma were used to calculate the probability of water deficits sufficient to cause plant water stress for each day of the growing season.
For the SWD method, the drought threshold was set at 50 percent depletion of the soil’s total available water capacity. For the AWD method, the threshold was a 7-d cumulative AWD of 50 mm, which researchers say provides an inferior prediction model than when soil water deficits are factored into the production model. In other words, researchers found that soil water deficits more successfully identified periods during which plants were likely to be water stressed than did traditional atmospheric measurements alone.
Calculating soil water deficits and stress thresholds for the eight study sites over a 15-year span, the research team compared their assessment of drought probability to assessments made using atmospheric data and found that when using atmospheric data alone, it often underestimated drought conditions, but when soil water deficit measurements were factored in, it provided a more accurate prediction model that consistently assessed drought probabilities.
But researchers warned soil moistures vary greatly from one site to the next, and each site must use specific soil moisture assessments in order to accurately improve the prediction model.
“The soil water contents differ across sites and depths depending on the sand, silt, and clay contents,” reports Ochsner. “Readily available water is a site and depth specific parameter.”
In some cases, soil measurements may not always be available, in which case calculations used for atmospheric assessments must be reconfigured. In two such instances, researchers decreased the threshold at which plants became stressed, allowing for a smaller deficit to be considered a drought condition. In addition, they increased the number of days over which atmospheric deficits were summed. After the changes, estimates better reflected soil water deficit probabilities.
The study found that drought is a major cause of limited productivity in rainfed agroecosystems throughout the world, accounting for a large proportion of the crop losses and yearly yield variation of annual crops. Drought costs are estimated to vary from $6 billion to 8 billion year in the United States alone, but single events have caused losses as high as $39 billion.
Drought is a climatological event characterized by low precipitation and intensified by weather factors such as low atmospheric humidity, high wind speeds, and high temperatures. Different types of drought are recognized, including meteorological, agricultural, and hydrological drought, each with specific characteristics and magnitudes.
The study defines meteorological drought as persistent below-average precipitation that can alter the seasonal replenishment of soil water, which may lead to agricultural drought. Agricultural drought is a deficiency in soil water that is severe enough to harmfully stress rangelands and pastures and to decrease crop production.
According to the OSU study, accurate assessment of seasonal patterns in drought probability is important because if the crop cycle can be matched with periods when drought is less likely to occur, yield losses due to drought may be reduced.
“We are in a time of rapid increase in the availability of soil moisture data, but many users will still have to rely on the atmospheric water deficit method for locations where soil moisture data are insufficient,” Ochsner says.
Ochsner and his team hope that their research will help farmers better plan the cultivation of their crops and avoid costly losses to drought conditions.