The effect of soil moisture anomalies on maize yield in Germany

Abstract

Abstract. Crop models routinely use meteorological variations to estimate crop yield. Soil moisture, however, is the primary source of water for plant growth. The aim of this study is to investigate the intraseasonal predictability of soil moisture to estimate silage maize yield in Germany. We also evaluate how approaches considering soil moisture perform compare to those using only meteorological variables. Silage maize is one of the most widely cultivated crops in Germany because it is used as a main biomass supplier for energy production in the course of the German Energiewende (energy transition). Reduced form fixed effect panel models are employed to investigate the relationships in this study. These models are estimated for each month of the growing season to gain insights into the time-varying effects of soil moisture and meteorological variables. Temperature, precipitation, and potential evapotranspiration are used as meteorological variables. Soil moisture is transformed into anomalies which provide a measure for the interannual variation within each month. The main result of this study is that soil moisture anomalies have predictive skills which vary in magnitude and direction depending on the month. For instance, dry soil moisture anomalies in August and September reduce silage maize yield more than 10 %, other factors being equal. In contrast, dry anomalies in May increase crop yield up to 7 % because absolute soil water content is higher in May compared to August due to its seasonality. With respect to the meteorological terms, models using both temperature and precipitation have higher predictability than models using only one meteorological variable. Also, models employing only temperature exhibit elevated effects.

Model-based reconstruction and projections of soil moisture anomalies and crop losses in Poland

“…For the above reasons, in this paper we use simulated SM data in order to develop proxy indicators of the occurrence of weather-related crop losses in Polish conditions. Previous SWAT applications in this context (Narasimhan and Srinivasan 2005;Wang et al 2016) as well as various empirical studies linking SM and crop yield for rain-fed agricultural systems in temperate climates (Kalbarczyk 2004;Koźmiński 1994;Peichl et al 2018) provide a solid support for such an approach.…”

“…In contrast to weather variables, soil moisture data have "memory", thus providing an integrated information on past meteorological conditions (Samaniego et al 2013;Urban et al 2015). As shown in the recent study of regression models of maize yields in Germany (Peichl et al 2018), accounting SM improved prediction capability for all investigated model configurations compared to regressions based only on meteorological variables. Similarly, simulated SM deficit indices had higher correlations with crop yields than conventional precipitationbased drought indices in studies in Texas (for wheat-see Narasimhan and Srinivasan 2005) and in the Upper Mississippi River Basin (for maize and soybean-see Li…”

Preface: Damage of natural hazards: assessment and mitigation

“…More and more studies, also the following two studies included in the special issue, use multivariate statistics or data-mining methods for data analyses and damage modelling (Figueiredo et al, 2018;Bachmair et al, 2017). Their main advantages are the ability to capture nonlinear, threshold, or nonmonotonic dependencies between predictor and response variables, to take interactions between the predictors into account and the possibility of being trained from data sets of various sizes (Rözer et al, 2016;Kreibich et al, 2017a;Wagenaar et al, 2017Wagenaar et al, , 2018.…”

“…Thus, the results may help the government to better allocate rescue funds. Peichl et al (2018) investigated the intra-seasonal predictability of maize yield using soil moisture information in Germany. The effects of soil moisture dominate those of temperature and are time dependent.…”

“…The special issue "Damage of natural hazards: assessment and mitigation" was inspired by the annual EGU session "Costs of Natural Hazards", which was organized in 2012 for the first time. This special issue presents 12 studies on advancements in the field of damage assessment and mitigation related to droughts (Bachmair et al, 2017;Peichl et al, 2018;Markantonis et al, 2018), extreme rainfall (Cortès et al, 2018;Spekkers et al, 2017), different types of floods (Markantonis et al, 2018;Cortès et al, 2018;Laudan et al, 2017;Bernet et al, 2017;Wagenaar et al, 2017) and earthquakes (Livaoglu et al, 2018;Yılmaz et al, 2018;Altunışık et al, 2017;Zhang et al, 2017). A common challenge tackled by many of the studies in this special issue is the lack of impact and damage data.…”