Seasonal dynamics in Wheel Load Carrying Capacity in Europe

Towards climate-smart sustainable management of agricultural soils SoilCompaC Mapping and alleviating soil compaction in a climate change context Deliverable 2.1 Seasonal dynamics in Wheel Load Carrying Capacity in Europe Due date of deliverable: M49 (February 2024) Actual submission date: M53 (June 2024) GENERAL DATA Deliverable 2.1: Seasonal dynamics in WLCC in Europe This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 2 Grant Agreement: 862695 Project acronym: SoilCompaC Project title: Mapping and alleviating soil compaction in a climate change context Project website: soilcompac Start date of the project: November 1st, 2021 Project duration: 36 months Project coordinator: Mathieu Lamandé DELIVERABLE NUMBER: D2.1 DELIVERABLE TITLE: Seasonal dynamics in Wheel Load Carrying Capacity in Europe DELIVERABLE TYPE: Report WORK PACKAGE N: WP2 WORK PACKAGE TITLE: Risk assessment of soil compaction in a changing climate DELIVERABLE LEADER: AU AUTHORS: Michael Kuhwald and Mathieu Lamandé SOILCOMPACCONTRIBUTORS: All partners ACKNOWLEDGEMENTS: Authors thank all project-partners who provided data for modelling. Deliverable 2.1: Seasonal dynamics in WLCC in Europe This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 3 1. Introduction Assessment of the risk of soil compaction is based on a comparison between the soil mechanical strength and the mechanical stresses applied to it (Figure 1). There is a risk of soil compaction if the soil stresses are larger than soil strength (Horn and Fleige, 2003). The external stresses depend primarily on the machinery used for a given operation, which is a characteristic of a production system. The soil strength depends on its intrinsic properties (i.e. particle size distribution), and properties that might change within few hours (soil matric potential), or within years to several decades (soil structure, SOC). A useful risk assessment requires a careful definition of appropriate scenarios for local soil conditions and production systems. For each pedo-climatic zone, we will investigate the effects of climate change on the wheel load carrying capacity and the number of trafficable days for critical agricultural operations. The overall approach was to perform model simulations for the 13 participating countries in order to evaluate the consequences of climate change on the risk of soil compaction for a range of pedo-climatic zones. All participants were involved in the preparation of data for the modelling by identifying the agricultural production system representative for their pedoclimatic zone(s). For the modelling framework, we used a similar scheme as proposed by Kuhwald et al. (2022) for seasonal assessment of compaction risks (Figure 2). Wheel load-carrying capacity (WLCC) is defined as the maximum wheel load for a specific tyre and inflation pressure that does not result in soil stress in excess of soil strength (van den Akker and Schjønning, 2004). Calculation of WLCC requires knowledge of soil strength, i.e. precompression stress (Horn and Fleige, 2003), from which the maximum allowable load can be simulated via an iterative algorithm to ensure soil stress ≤ soil strength (Gut et al., 2015). In this work, we concentrate on prevention of subsoil compaction, i.e. calculating WLCC based on the condition subsoil stress ≤ subsoil strength. Hereby, soil precompression stress is estimated with help of pedo-transfer functions from easily-available soil attributes (e.g. soil texture), soil moisture (by modelling), and further soil characteristics if available (e.g. soil structural descriptions; Lebert & Horn, 1991). In summary, seasonal dynamics of soil moisture can be “translated” with help of pedo-transfer functions to seasonal dynamics in soil precompression stress, which in turn can be “translated” to maximum allowable loads (i.e. wheel load carrying capacity) with help of iterative soil stress simulations – as outlined in Figure 1. The computed seasonal WLCC can then be compared with real wheel loads of agricultural vehicles to obtain the number of trafficable days for different agricultural vehicles (similar as in Gut et al., 2015). Calculations are made for each pedo-climatic zone, using future climate projection data (20202100) and ten different climate models (CMIP6) for two scenarios (SSP126 and SSP585). These climate data were used to run a soil-crop-atmosphere model that provided the seasonal dynamics of the soil matric potential for each pedo-climatic zone, which was needed for the assessment of soil compaction risk (Fig. 1.). Deliverable 2.1: Seasonal dynamics in WLCC in Europe This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 4 Figure 1: Computation of wheel load carrying capacity (WLCC), i.e. the maximum wheel load that will not induce soil compaction. Soil moisture dynamics (matric potential, soil water content) is simulated with help of a soil-crop-atmosphere model for different pedo-climatic zones. Soil moisture, together with basic soil information, is used to calculate soil strength by means of pedotransfer functions (1), which is used to calculate wheel load with an iterative algorithm that ensures soil stress ≤ soil strength (2). Comparison of WLCC with actual wheel loads of farm vehicles yields the number of trafficable days (shaded area, 3). Figure 2: Structure of the SaSCiA-model for calculation of the soil compaction risk (Kuhwald et al. 2022) Deliverable 2.1: Seasonal dynamics in WLCC in Europe This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 5 2. Modelling framework To model the wheel load carrying capacity (WLCC) we used the SaSCiA-model (Spatially explicit soil compaction risk assessment) published in Kuhwald et al. (2018, 2022) (Figure 2). As this model was developed to calculate the soil compaction risk at a broader spatial scale for single years, we made some model adjustment. First, the model was reorganized to a point scale model. Second, the model was further developed to enable a long-term modelling. Within the model, the soil strength is calculated by Horn and Fleige (2003) and DIN V 19688. To account for the strong effects of soil water content on soil strength, the equations by Rücknagel et al. (2012, 2015) were used. Therefore, the soil moisture is necessary. Within the SaSCiA-model the soil-crop model “MONICA (Nendel et al. 2011; ZALF) is integrated to calculate the daily soil moisture depending on crop type, soil type and weather conditions. The result is the moisture depended daily soil strength (or precompression stress) for each side for each depth. The soil stress is calculated by Koolen et al. (1992) and Rücknagel et al. (2015). As the soil stress propagation into the soil is also moisture dependent, the “concentration” factor is used. Based on the calculated soil moisture with the MONICA-model, a daily “concentration” factor is modelled and used for soil stress determination. Due to the WLCC-concept, soil compaction is expected when soil stress is higher than the soil strength. Thus, the threshold for soil stress was set at that level of soil strength. By conversion of the formula by Koolen et al. (1992), the associated wheel load can be calculated for each soil strength. The only limitation, however, is that the tire inflation pressure and the soil depth have to be set to certain values. We used a tire inflation pressure of 150 kPa and a soil depth of 35 cm for all calculations. The final results are daily maximum wheel loads for each study site. 3. Input data for the modelling a. Soil data, crop rotation, machinery setup Each partner selected a representative pedo-climatic zone from his country. We used the “Environmental” zone map from Metzger et al. (2005) to identify the different zones within the EU (e.g. Continental, Nemoral, Boreal). To delineate the soil region, we selected the EU-soil map (EGDI). Finally, we had 12 different pedo-climatic zones for WLCC modelling (Table 1). For each pedo-climatic zone the partners collected soil data in form of a soil profile, the typical crop rotation, applied agricultural practices, used machinery and machinery setup (e.g. wheel load, typical tyres). This data was used for WLCC modelling. Deliverable 2.1: Seasonal dynamics in WLCC in Europe This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 6 Table 1: Pedo-climatic zones used for wheel load carrying capacity (WLCC) modelling Number Country Environmental zone Soil zone Crop rotation 1 Austria (1) Pannonian 153 (Chernozem) rapeseed – winter barley – soybean – winter wheat – soybean – winter barley – winter wheat – spring durum wheat – winter barley 2 Austria (2) Continental (CON7) 121 (Stagnosol) rapeseed – winter wheat – silage maize – winter wheat - silage maize – winter barley 3 Austria (3) Continental (CON2) 200 (Cambisol) winter barley – summer oat – winter wheat – winter triticale - soybean 4 Denmark Atlantic north 81 (Umbrisol) winter wheat – spring barley – field pea 5 Estonia Boreal 55 (Luvisol) field peawinter wheat – spring barley – rapeseed – winter rye 6 Germany Atlantic North (ATN4) 111 (Luvisol) silage maize – winter wheat – sugar beets – winter wheat 7 Lithuania Nemoral 42 (Luvisol) spring wheat – spring barley – field pea – winter wheat - rapeseed 8 Netherlands Atlantic central (2) 102 sugar beets – spring barley – potato – winter wheat 9 Spain MediterraneanSouth 248 (Calcisol) winter wheat – field pea – winter barley - fallow 10 Sweden Continental (9) 132 (Cambisol) winter wheat – winter wheat – sugar beets – spring barley - rapeseed 11 Switzerland Continental (8) 121 (Luvisol) winter wheat – rapeseed – winter wheat – silage maize 12 Turkey Anatolian Calcisol* silage maize – winter wheat * is not available on the environmental and soil zone maps (https://data.geus.dk/egdi/) b. Historical climate data In order to compare historical WLCC with future WLCC, each partner tried to get measured weather data for his pedo-climatic zone. However, this task was challenging and thus not for all 12 pedo-climatic zones this data is available. Deliverable 2.1: Seasonal dynamics in WLCC in Europe This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 7 c. Future climate projection data To model the future WLCC, free available climate scenario (CMIP 6) data was used. To account for the high variation in climate predictions, we selected 10 different climate models (Table 2) for our modelling. To show the effects of different emission rates of CO2 and accomplished measures in society, we selected the SSP126 (best case) and SSP 585 (worst case) scenarios (O’Neil et al. 2016; Riahi et al. 2017). Required variables are: temperature (minimum, mean, maximum), precipitation, relative humidity, wind speed, solar radiation. The datasets are available as world maps (nc-files) with a daily resolution, typically from 2015 to 2100 (or longer). An algorithm was developed to receive the necessary data for each pedo-climatic zone out of these world-maps. In total, 20 modelling of WLCC were conducted for each pedo-climatic zone. Table 2: Used climate models for wheel load carrying capacity (WLCC) modelling Source ID Variant label Spatial resolution (km) ACCESS_CM2 r1i1p1f1 250 CanESM5 r1i1p1f1 500 CESM2 r11i1p1f1 100 CMCC_ESM2 r1i1p1f2 100 INM_CM5 r1i1p1f1 100 IPSL_CM6 r1i1p1f1 250 MIROC6 r1i1p1f1 250 MPI_ESM1_2 r1i1p1f1 250 MRI_ESM2 r1i1p1f1 100 NorESM2 r1i1p1f6 100 4. Effects of climate change on the number of trafficable days for each pedo-climatic region Figure 3 shows exemplarily the result for one pedo-climatic zone (Austria 2). It compares the WLCC between the best case (SSP126) and the worst case (SSP585) in the short-term (20202050) and in the long-term (2051-2100). For that, the mean values from all 10 modelling runs were calculated for each SSP for both time periods. The x-axe gives the days within the year, while the y-axe shows the maximum wheel load (in kg) before soil compaction is expected. Deliverable 2.1: Seasonal dynamics in WLCC in Europe This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 8 Figure 3: Variation of the wheel load carrying capacity (WLCC) for one of the pedo-climatic zones comparing two scenarios (SSP 126 and 585) and the shortand long-term-effects (depth: 35 cm; tier inflation pressure: 150 kPa) For both periods, lowest WLCC is during winter and early spring (December to April). From April on the WLCC increases and reaches its maximum in September. Afterwards it decreases again. Thus, the WLCC-curve represents the combination out of weather conditions and plant growth, with high precipitation and low transpiration in winter which results in high soil moisture at that time. Increased crop transpiration from April on decreases the soil water content and thus increases the WLCC. In the short-term, there is nearly no differentiation between both scenarios. Only in summer the SSP585 leads to slightly higher WLCC. In the long-term, however, significant differences between best-case and worst-case scenario exist. While in winter there is a slight decrease in WLCC, from April to mid of November there is an increase of WLCC of up to 29 %. Deliverable 2.1: Seasonal dynamics in WLCC in Europe This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 9 The same development of WLCC for both scenarios can be observed for nearly all of the 12 pedo-climatic zones. Summarizing the changes of WLCC from all 12 pedo-climatic zones gives an overall idea of future changes in soil compaction risk, which is shown in Figure 4. It is again divided in to short- (blue) and long-term (orange) effects and compares the best case with the worst-case scenario. In shortterm, the maximum changes are a decrease of WLCC in May up to 3.5 % and a maximum increase in October of 4.9 %. In the long-term, only an increase of WLCC exist with a maximum of 22 % in October. Figure 4: Changes in wheel load carrying capacity comparing SSP 126 with SSP585 for all pedo-climatic zones for the short- (blue) and long-term (orange) Summarising the modelling results there is a clear trend in future WLCC. Assuming the bestcase scenario (SSP126), only little changes in WLCC and thus in soil compaction risk will occur. Following the worst-case scenario (SSP 585), WLCC will continuously increase and the soil compaction risk will decrease. However, this are the mean trends and two important points have to considered. The first one is the extremely high variation between the year. For instance, three dry years with high WLCC can be followed by a wet year with low WLCC. If soil compaction occurred in this wet year due to heavy wheel load, the negative effects on soil functionality will stay for decades. Therefore, the long-term trend cannot be used for decision making on single years. The second point is the high variation between the different models. Figure 5 shows in example for Switzerland. Depending on the selected model, the WLCC for the same period can vary between 1 Mg and 7 Mg. This reflects the uncertainty in climate projections which have also large effects on WLCC modelling.