Estimating impact of weather factors on wheat yields by using panel model approach — The case of Serbia

Abstract

In Serbia irrigation is not widely utilized to reduce water scarcity in crop production. Therefore, wheat yields largely depend on weather factors. Over the past two decades, there has been recorded a significant change in weather conditions in Serbia. Such change produces concerns about Serbia's food security and exports since wheat is among the most important agricultural products. In this paper authors analyze and quantify the impact of weather factors on the achieved wheat yields, using a set of panel data on selected Serbian municipalities in fourteen years (2000-2013). The multidimensional regression was conducted as a sort of quasi-experiment, combining data on achieved yields in selected municipalities, with data on weather factors: temperature, precipitation, extraterrestrial radiation, and evapotranspiration. Utilizing the Hargreaves method of determining reference evapotranspiration, average daily water deficit was computed as a single representative indicator of weather conditions. Testing was conducted on four predefined sub-periods within the vegetation season of wheat, and the impact of average daily water deficit on wheat yields was estimated for each of these sub-periods. Results show a robust, statistically significant impact of change in average daily water deficit on decreased wheat yields. Growth of water deficit by 0.1 mm, in the period November 15th to April 1st results with 175 kg/ha lower yields, while in the period April 1st to May 15th results in 45 kg/ha lower yields. Impact shows to be conditional on the altitude, rapidly losing on intensity and significance above 100 m.

Evaluation of and Variability in Yields and Yield Components of Wheat Cultivars in Northern Serbia

“…Serbia. Therefore, wheat yields largely depend on weather conditions (Jeločnik et al, 2019). The favourable annual precipitation distribution is a distribution that provides crops with the required amounts of water for their growth and development, particularly during the critical phenological stages such as flowering, seed setting and grain-filling.…”

Impacts of climate change on winter wheat and summer maize dual-cropping system in the North China Plain

“…The correlation coefficients were low, and the VIF value of each explanatory variable also did not exceed 10, which was within a reasonable range. Neither analyses showed serious multicollinearity among the variables in the model (Xiao et al 2018, Jeločnik et al 2019, indicating that the influence of excessive collinearity on the final results could be ignored. Thus, the individual and time fixed effects panel regression model was determined to be the most reasonable equation.…”

“…Thirdly, necessary analyses were implemented. The unit root test was applied to check the stationarity of data (Jeločnik et al 2019), showing that all variables of winter wheat and summer maize were stationary. The Hausman test to determine the model types suggested that the fixed effects model was more appropriate (Jeločnik et al 2019).…”

“…The unit root test was applied to check the stationarity of data (Jeločnik et al 2019), showing that all variables of winter wheat and summer maize were stationary. The Hausman test to determine the model types suggested that the fixed effects model was more appropriate (Jeločnik et al 2019). We calculated the correlation coefficients (tables S1 and S2) and the variance inflation factor (VIF) (table S3) of the variables to measure the multicollinearity among the variables.…”

Yield potential of world wheat based on ARIMA model under global warming

“…Possible impacts of three climate variables on spring wheat yield in North Dakota of USA was assessed by building a regression model, which showed that the percentage deviation of error for the model is approximately +/-30% in most of the years (Mistry and Bora, 2019). The impact of weather factors on the achieved wheat yields was analyzed by using a set of panel data on selected Serbian municipalities in 2000 to 2013, which displayed that the growth of water deficit by 0.1 mm in the period November 15 to April 1 resulted in 175 kg ha -1 lower yields while in the period April 1 to May 15 did in 45 kg ha -1 lower yields (Jelocnik et al, 2019). The derived phenological metrics for vegetation indices (VIs) and surface reflectance's (SRs), namely peak, area under curve (AUC), and fitting coefficients from a quadratic function, were used for building empirical regression winter wheat models at a regional scale in Ukraine for three years (2016)(2017)(2018), yielding a RMSE of 0.201 t ha -1 (5.4%) and coefficient of determination R 2 of 0.73 on cross-validation (Skakun et al, 2019).…”