Abstract
Although frost can cause considerable crop damage, and practices have been developed to mitigate forecasted frost, frost forecasting technologies have not changed for years. This paper reports on a new method based on successive application of two models to forecast the number of monthly frost days for several Community of Madrid (Spain) meteorological stations. The first is an autoregressive integrated moving average (ARIMA) stochastic model that forecasts minimum monthly absolute temperature (t min) and average monthly minimum temperature (micro t) following Box and Jenkins methodology. The second model relates monthly temperatures (t min, micro t) to the minimum daily temperature distribution during one month. Three ARIMA models were identified. They present the same seasonal behaviour (integrated moving average model) and different non-seasonal part: autoregressive model (Model 1), integrated moving average model (Model 2) and autoregressive and moving average model (Model 3). The results indicate that minimum daily temperature (t dmin) for the meteorological stations studied followed a normal distribution each month with a very similar standard deviation through out the years. This standard deviation obtained for each station and each month could be used as a risk index for cold months. The application of Model 1 to predict minimum monthly temperatures produced the best frost days forecast. This procedure provides a tool for crop managers and crop insurance companies to assess the risk of frost frequency and intensity, so that they can take steps to mitigate frost damage and estimate the damage that frost would cause.
Highlights
Crop establishment and development are crucial because of the investment of time and resources involved
The tmin series fitted two stations (m109 and m195), a water reservoir (m109) and the city centre surrounded by forest and vegetation (m195), a differentiating it from other stations in the same area. Both time series fitted the station located at Barajas airport, with its higher instability due to winds and turbulence created by aeroplanes
The forecast for minimum temperatures is in good agreement with the data
Summary
Crop establishment and development are crucial because of the investment of time and resources involved. Since freezing temperatures restricting the length of the growing season are responsible for reductions in yield and quality of agricultural crops (Harker, 2002; Faubion, 2003), minimum temperatures become critical. Many studies have used monthly temperature, rain, radiation, etc. Data to develop models that simulate agro-climatic scenarios (Kuehl et al, 1976; Andersen et al, 2001). Studies with regression functions by Cao and Moss (1989), Jamieson et al (1995) and Landau et al (2000), among others, highlight the relevance of temperature in crop growth. Several temperature models have been developed in order to simulate crop response to daily maximums and minimums of one specific region (Jamieson et al, 1995), or with lower accuracy, response to monthly temperatures (Nonhebel, 1993; Castellanos, 1997) or annual oscillations (Fernández, 1992)
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