Effects of a maize- soybean strip intercropping system on canopy level wind velocities in a semi-humid region of Central Europe

Effects of a maize- soybean strip intercropping system on canopy level wind velocities in a semi-humid region of Central Europe | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effects of a maize- soybean strip intercropping system on canopy level wind velocities in a semi-humid region of Central Europe Günther Gollobich, J. Eitzinger, J. Friedel, H. Wagentristl, P. Weihs, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8059550/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Intercropping has been proposed as an alternative to conventional monoculture systems by improving biomass water use efficiency (WUE) and grain yield based WUE through modified microclimatic conditions. However, little is known about its potential to mitigate wind velocities (WV) in semi-humid Central European environments. A two-year field experimental case study (2023–2024) was conducted to assess wind-breaking effects in a maize–soybean strip intercropping system. Treatments included sole crop and strip-intercropping plots, where microclimatic and crop data were collected. WV and wind direction (WD) of the treatments were measured at 1m and 2m above ground. Reference 2m above ground WV from the Integrated Nowcasting through Comprehensive Analysis (INCA) in both years were used for comparison between the two years. Wind reduction effects (WRE) were further analyzed in relation to wind direction sectors (WS). Strip intercropped plots showed significantly reduced wind velocities in respect to the 2m INCA reference, with the strongest effects in soybean (SS) and maize strips (MS) at 1m measurement height. In 2023, mean reductions ranged from 0.6–1.0 m/s (SS) and 0.5–1.4 m/s (MS), while in 2024, reductions ranged from 0.7–1.2 m/s (SS) and 0.7–1.1 m/s (MS). The highest WRE occurred in the climatological main wind directions (270–360° in 2023; 315–360° in 2024), consistent with the north–south strip and row orientation. Both, above ground dry biomass and grain yield based WUE calculations largely confirmed positive effects of wind reduction with some differences between 20203 and 2024 due to weather conditions. Wind reduction supports not improved grain yield based WUE (2023: -7.7 kg/m³ (SS), -21.8 kg/m³ (MS); 2024: -21.7 kg/m³ (SS), -13.4 kg/m³ (MS)) in comparison to mono cropped plots. The biomass WUE compared to the mono cropped plots was in 2023 positive (+ 14.9 kg/m³ (SS) and 58.1 kg/m³ (MS), while in 2024 the biomass WUE was negative (-181.7 kg/m² (SS) and − 192.3 kg/m³ (MS)). The outcomes are influenced by strip width and crop-specific interactions (i.e. biomass WUE), highlighting the importance of strip design such as strip orientation in respect to main wind directions for optimizing microclimatic benefits. agrometeorology adaption measure microclimatic conditions strip intercropping wind reduction effect Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1 Introduction Water is essential for plant growth and yield formation and represents a limiting factor, especially in arid and semi-arid regions (Gao et al., 2013 ). In the context of climate change, water scarcity is expected to increase, particularly in these regions (Rahman et al., 2017). Limited water availability is also one of the main factors contributing to declining agricultural crop productivity (Feng-Yun et al., 2012 ). Global climate change is projected to result in higher evapotranspiration rates, reduced precipitation, and a greater frequency of extreme rainfall events (Rahman et al., 2017). These adverse effects may push agricultural production in many regions closer to critical thresholds (Rasedzuzzaman et al., 2016). Therefore, improving WUE or biomass / grain yield based WUE through adapted crop management practices has become a key strategy to ensure sustainable use of natural resources, including the implementation of intercropping systems (Zhao et al., 2019 ). Intercropping—the simultaneous cultivation of two or more crop species on the same field—allows for complementary use of water and nutrients in both space and time (Salehi et al., 2018; 2017) and has been shown to increase land productivity (Gao et al., 2013 ; Willey, 1990 ). Sustainable improvements in WUE and land productivity are therefore central objectives in modern agricultural strategies (Wallace, 2000 ; UN, 2024), particularly in the face of increasing competition for water resources (Raza et al., 2022). Synergistic effects in water use, such as reduced competition and enhanced compensation mechanisms, are essential for the sustainability of intercropping systems in water-limited environments (Chen et al., 2014 ). Previous studies have shown varying effects of intercropping on WUE (Mao et al., 2012 ). One specific form, strip intercropping of two crop types of different canopy height, offers the additional benefit of wind protection for the lower height crop, while enabling more flexible field operations compared to permanent windbreaks (Radke & Hagstrom, 1976 ). Furthermore, the geometry of strip intercropping can significantly influence WUE, either positively or negatively, compared to sole cropping systems (Rahman et al., 2017, 2017). Optimized strip intercropping designs have been reported to save 20–50% of water (or land equivalent), especially under limited soil water availability and increasing drought risk due to climate change (Raza et al., 2022). Recent findings suggest that maize–soybean strip intercropping improves both water and nitrogen use efficiency and enhances land productivity under irrigated conditions (Raza et al., 2021). Similar benefits have been observed under rainfed conditions in arid and semi-arid regions, leading to more stable yields and higher WUE (Yin et al., 2020 ; Mao et al., 2012 ). Integrating legumes into maize rows—either in-row or as strips—has also shown promise for improving productivity for smallholder farmers under rainfed conditions (Thinley et al., 2024 ). Increased frequency and severity of agricultural droughts as well as heat waves are expected under future climate scenarios, including Central Europe. This trend is particularly relevant in the Marchfeld region near Vienna—one of Austria’s eight major agricultural production areas (Strauss et al., 2012 ; Thaler et al., 2008; INSPIRE, 2024 ). Future projections indicate a growing demand for irrigation in this region (Kirchner et al., 2012 ), raising concerns about groundwater depletion (Neudorfer & Weyermayer, 2007 ). Consequently, research attention has shifted towards assessing water consumption and WUE in field crop systems within Austria’s drought-prone regions (Thaler et al., 2010; 2012; Neugebauer et al., 2014). Given the predicted changes in climatic conditions and their effects on crop–soil water balance, the question arises whether adapted cropping systems such as strip intercropping could positively influence microclimatic conditions (i.e. WV, WUE) and thus improve the crop–soil water balance. In light of increasing agricultural drought in Eastern Austria, particularly in the Marchfeld region with its relatively high wind loads, a two-year field experiment was conducted to address the following three main research questions: a) To what extent, and in what way, does a specific strip intercropping design influence wind velocities within the windbreaking range of the strips at canopy level? b) How crop water use efficiency (biomass and grain yield based) of the both strip crops (soybean and maize) is affected? c) How wind reduction effects are related to wind direction over two growing seasons? 2 Materials and Methods Site characteristics The strip intercropping field experiment was conducted in the Marchfeld region, a major agricultural production area near Vienna, Austria. The experimental site is located southwest of Raasdorf (48° 13′ 58.98″ N, 16° 35′ 30.984″ E) at an altitude of 156 m above sea level (asl) and has a flat exposition (0° slope). A hedgerow, approximately 4 m in height, was situated about 30 m north of the experimental field. This location in the Marchfeld region is characterized by its continental climate, typically experiencing hot, dry summers and cold winters with minimal snowfall, making it an ideal setting for studying agricultural adaptations to drought-prone conditions. Climate characteristics Climate characteristics were derived from data series of a nearby World Meteorological Organization (WMO)- standard weather station in Groß-Enzersdorf (source: Geosphere Austria, 2023 and 2024). For the maize and soybean growing season May- June-July-August- September (MJJAS), the precipitation sum in 2023 was 323 mm, which was equal to the long-term mean (LTM) of the period 2003–2024 with 323 mm. The total precipitation in 2023 was 685 mm and showed an increase of 148 mm compared to the LTM of 537 mm. The mean annual temperature (MAT) for 2023 was 12.3°C, which was 1.3°C above the LTM (2003–2024) of 11°C. In comparison, the MJJAS precipitation sum in 2024 of 450 mm was well above the LTM of the period 2003–2024 (+ 127 mm), caused primarily by a heavy precipitation event in early September at the end of the crop growing season. The total precipitation in 2024 was 648 mm and showed an increase of 111 mm compared to the LTM (Fig. 1 ). The MAT in 2023 of 12.7°C was 1.7°C above the LTM (2003–2024), while the MAT in 2024 of 10.9°C was 0.1°C below the LTM (2003–2024). The MJJAS mean temperature in 2023 was 19.7°C and was 2.5°C higher compared to the LTM (2003–2023). In comparison, in 2024, the MJJAS mean temperature was 18.5°C and was 1.3°C higher than the LTM temperature (Fig. 1 ). Over the growing season (Fig. 2 ), the precipitation in June 2023 was 34 mm above the LTM (2003–2024). In July, the precipitation amount was 45 mm below the LTM. In August, it was 31 mm above the LTM and showed a deficit of 36 mm in September. October also recorded an increase of 12 mm compared to the LTM. In the growing season of 2024, the precipitation in June was 21 mm above the LTM (2003–2024). In July, the precipitation amount was 40 mm below the LTM. In August, it was also 17 mm below the LTM, and in September, due to an extremely heavy precipitation event, a surplus of 155 mm above the LTM occurred. October 2024 showed a deficit of 4 mm compared to the LTM (Fig. 2 ). Soil characteristics According to the WRB classification (IUSS Working Group WRB, 2022 ), the soil type can be classified as Chernozem, composed of calcareous fine sediments ( www.bodenkarte.at ; EBOD2; Aust et al., 2023). This soil type is well-supplied with water, has a good water storage capacity, and exhibits moderate permeability. The crop-available soil water capacity from 0–1 m soil depth ranges between 140–220 mm. The horizons of this soil type are predominantly A1/A2, sometimes A3, with a final C horizon. The following soil characteristics describe the investigated soil profile: clayey silt (Ul; 44%), loamy sand (Ls;16%), or sandy loam (Sl). The C horizons contain either Lt or sand (S; 40%) as their soil characteristics. The soil is calcareous (7.5 pH in 0.01m CaCl2) and easy to cultivate, making it high-quality farmland (Fig. 3 ). Experimental plot locations and treatments The field trial (42m x 46m) in 2023 with a total area of 0.19 hectares (Fig. 4 c), consisted of 12 maize monocrop plots, 8 soybean monocrop plots, and 8 maize-soybean strip plots, all with the same size of 60 m2. The intercropping design consisted of 28 plots, arranged in four replications. Each strip plot included alternating strips of soybean and maize, with each strip being 1.5 meters wide and a row width of 50 cm, while the orientation of the strips was from north to south. This setup enabled a systematic comparison between the intercropped system and monoculture plots within the experimental layout. The locations of the plots (T1-T7) within the block (B1-B4) were randomized. The field trial in 2024 had almost the same design (total area of 0,16 hectares), consisting of 12 maize monocrop plots, 4 soybean monocrop plots, and 12 maize-soybean strip plots of 60 m2 each, but with a different (randomized) distribution of plots. Compared to 2023, the location of the field trial in 2024 was shifted 45 m south within the same field but keeping the same intercrop plot design. The sowing density (maize and soybean) was for both year equal (35–55 seed grain/ m²). The WV and WD within the measurements periods of both experimental years are shown in Fig. 4 a (2023) and Fig. 4 b (2024), confirming the main WD from north-west and south-east, with higher mean wind velocities occurring in the 2024 measurement period. In both years the daily mean WV from the Integrated Nowcasting through Comprehensive Analysis (INCA) grid data (Haiden et al., 2011 ), hosted by Geosphere Austria, of the experimental site (the spatial explicit 1 km 2 grid) were used as the reference. For example, this dataset is used, among others, the Agricultural Risk Information System (ARIS) for crop water balance monitoring (Eitzinger et al., 2024 ). In addition, the INCA dataset was validated with the local reference stations REF 1 and REF 2 in both measurement periods. The INCA dataset represents WMO standard surface conditions (open area, short grass) of the wind profile. In 2023, sowing of both crops (maize and soybean) was carried out on June 20th (delayed due to a crop failure at the first sowing attempt), and the microclimatic measurements in the selected plots (Fig. 5 c,d) started on August 5th, 2023 (DOY 217). The microclimatic measurements for the validation of the INCA grid dataset (Fig. 5 a, b) at the reference plot (REF1) began after a first sowing attempt on May 24th, 2023 (DOY 145) and were placed in the middle of the plots or strips. Soybean was harvested on 11th October and maize on 23rd October. The delayed sowing and harvesting date in 2023 were caused by extreme (cold and wet) weather conditions, which has to be considered as an unusual and rare cropping period in practice. In 2024, all microclimatic measurements started on June 1st, immediately after sowing of both crops simultaneously (30th April). In the second experimental year 2024, a similar measurement setup with the same sensor equipment as in the first year was applied, maintaining the same design measurement height above ground level (MH) of microclimatic variables and for WV measurements to compare two years of different weather conditions. In the second year, the reference weather station was placed in the center of a soybean monocrop plot within the strip intercropping experiment. Both crops were harvested on 11th September. Field measurements and microclimatic variables In 2023, the experimental setup included 6 agrometeorological weather stations (W1- W6), (Fig. 5 a-d). For 2024, the weather stations setup remained the same. Details of the data acquisition equipment, including type of data logger, specific sampling interval for each sensor, and logging intervals are listed in Table 5 ( Appendix ). The measured variables at each location within the field trial are listed in Table 6 ( Appendix ). Crop development and above ground dry biomass Sowing of maize and soybean in the strip intercropping experiment was conducted on the same day each year, although the sowing dates were differed between the two experimental years (June 20, 2023, and April 30, 2024, for both crops, respectively). In the observed years, plant heights were measured at five observation dates: Day of the year (DOY) 185–283 in 2023 and DOY 206–255 in 2024). The development of the plant height (Fig. 6 ) of both crops (maize and soybean) in 2023 was accelerated due to delayed sowing. To determine the above ground dry biomass (AGDB) and grain yield (at harvest) of both crops, samples were taken and related to the cropped area in tons per hectare. In addition, the growth stages of both crops were taxed by the BBCH phenological status to get information about the growth process over the two observed years. Precipitation based water use efficiency The AGDB (above ground dry biomass) WUE based on precipitation was calculated using the following Eq. 1, (Briggs et al., 1913): AGDB WUE = (AGDB [t/ha]) / (precipitation [mm]) (1) The grain yield based WUE was calculated using the following Eq. 2, /Briggs et al., 1913): Grain yield based WUE = (grain yield [t/ha]/ (precipitation [mm]) (2) For the calculation of the WUE in both years (2023–2024) the differing reference data sets were used. As for 2023 the both strip crops growth period was from (DOY 185–283) and in 2024 from DOY 206–255 the results are not directly comparable in respect to a “theoretical” same seasonal crop growth period (which is, however, in practice variable as well from year to year due to changing annual weather conditions ). In our case there was no irrigation within the experimental field and so the measured precipitation for the calculation was used. Statistical analysis tools The main statistical analysis was done by EXCEL (2023) sheets (. The logged data from the ZL6 data loggers was processed with the standard functions in EXCEL (minimum, maximum, mean, standard deviation). The gap filling and pre- analysis were also done by EXCEL. Concerning the wind roses and the processing of the other datasets the following r- packages were used: “openair”, “psych”, “summary tools”, “corrplot”, (Carslaw&Hopkins, 2012), (Revelle, 2024 ), (Comtois, 2024 ), (Wei&Simko, 2021). The packages were integrated within R-studio 4.3.2, (R-studio team, 2024). 3 Results Wind velocities within the strip intercropping variants WD and WV in Table 1 compares the measurement results of WV at 1m and 2m measurement height (MH) and the statistical values of the different variants, i.e., the reference dataset (INCA), the soybean strip (SS) and maize strip (MS The data from the WV measurements during the field experiment of both years (2023, 2024) were compared. In addition, the WV data were divided into 4 WS to get an overview of the distribution of the WV for each sector. Table 1 Overview of the wind velocity statistics (range, mean, standard deviation (±) based on 10- minute measurements for the reference dataset (INCA) and each investigated plot (SS (soybean strip) and MS (maize strip) in measurement height (1 m, 2 m)). Plot Range 2023 1 * Range 2024 2 * Mean 2023 1 * Mean 2024 2 * INCA dataset (1m MH) 0.6–4.8 0.6–5.7 1.7 ± 0.8 1.8 ± 0.9 INCA dataset (2m MH) 0.7–5.7 0.7–6.9 2.0 ± 1.0 2.2 ± 1.1 Soybean strip plot (1 m MH) 0.1–0.3 0.2–2.1 0.1 ± 0.1 0.6 ± 0.5 Soybean strip plot (2 m MH) 0.4–4.3 0.5–3.9 1.0 ± 0.7 1.6 ± 0.9 Maize strip plot (1 m MH) 0.2–0.7 1.1–4.5 0.3 ± 0.1 0.5 ± 0.4 Maize strip plot (2 m MH) 0.4–4.3 0.5–3.2 1.1 ± 0.7 1.3 ± 1.0 1 Measurement period 2023: DOY 217–283; n = 14.112 2 Measurement period 2024: DOY 206–255; n = 7056, *all values in [m/s] In the observation year 2023, the WV differences between the MS plot and the INCA dataset were 0.7 m/s at 2m MH and 1.3 m/s at 1m MH. In the observation year 2024, the results were comparable: on the MS plot, the WV was 0.7 m/s at (2m MH) and 1.2 m/s at (1m MH). The mean in 1m measuring height (MH) shows a large deviation (mean) in the intercropping plots. The results from 2023 on the SS plot were 1.5 m/s at (1m MH) and 0.8 m/s at (2m MH). In 2024, the WV on the SS plot was 1.2 m/s at (1m MH) and 0.7 m/s at (2m MH) (Table 1 ). Comparing the results of the mean WV in both observation years (see Table 1 ), the INCA dataset show the highest mean WV at (2m MH) as well as at (1m MH) as expected. For comparison, the main wind directions during the years 2021–2024 were in the NW-sector (29%), West (20%) and SE-sector (16%) according to measurements at 10m height at the representative Geosphere Austria weather station in “Groß-Enzersdorf” (Fig. 7 ). Figure 9a-f: Comparison of the daily mean wind velocities at different measuring heights for each observed plot (REF- soybean monocrop, MS, SS) over the entire measurement period in 2024, combined with different wind sectors: a) wind velocities on the REF2 plot at 2 m MH; b) wind velocities on the MS plot at 2 m MH; c) wind velocities on the SS plot at 2 m MH; d) wind velocities on the REF plot at 1 m MH; e) wind velocities on the MS plot at 1 m MH; f) wind velocities on the SS plot at 1 m MH. The wind directions are divided into 4 sectors: Sector 1 (0–90°), sector 2 (91–180°), sector 3 (181–270°), and sector 4 (271–360°). These sectors correspond to the main geographic directions. (DOY 217–270); n = 7776; Gray dots represent outliers. In 2023, at 2m MH, the order of the WV was 4 > 3 > 2 > 1 within the INCA grid dataset, 3 > 4 > 2 > 1 on the MS plot, and 3 > 4 > 2 > 1 on the SS plot. At 1m MH, the distribution was 2 > 4 > 3 > 1 on the REF plot, 2 > 4 > 3 > 1 on the MS plot, and 2 > 4 > 3 > 1 on the SS plot. The lowest WV occurred across all MH and plots in WS 1 (wind from north-east). In 2024, at 2m MH, the order of WV was 4 > 2 > 3 > 1 within the INCA grid dataset, 4 > 3 > 2 > 1 on the MS plot, and 4 > 1 > 3 > 2 on the SS plot. At 1m MH, the order was 3 > 4 > 2 > 1 on the REF plot, 3 > 2 > 4 > 1 on the MS plot, and 4 > 3 > 2 > 1 on the SS plot. In 2024, the lowest WV also occurred in the WS, 1, except for the SS plot at (2m MH), where the dominating WS was 2. Wind reduction effect within the strip intercropping variants To determine WRE, the WV measured on the REF plots were compared to the WV measured within the strips of MS and SS plots (Table 2 ). WRE (see Table 2 ) were observed in all strip variants and measurement heights with respect to the monocrop plots (MS; SS), with distinct differences between the four WS, (Fig. 8 a-f and Fig. 9a-f). Table 2 Overview of the wind reduction effect for the intercropping plots (MS, SS) compared to the reference dataset (INCA) for the 2023 and 2024 experimental seasons at 1 m and 2 m wind measurement heights (based on Table 1 ). Plot Range 2023 1 * Range 2024 2 * Mean 2023 1 * Mean 2024 2 * Soybean strip plot (1 m MH) 0-4.8 0-3.1 1.0 ± 1.1 1.2 ± 0.7 Soybean Strip plot (2 m MH) 0-2.3 0–2.0 0.6 ± 0.6 0.7 ± 0.5 Maize strip plot (1 m MH) 0.4-4.0 0-3.2 1.4 ± 0.8 1.1 ± 0.8 Maize strip plot (2 m MH) 0-2.2 0-2.21 0.5 ± 0.6 0.7 ± 0.5 1 Measurement period 2023: DOY 217–283; n = 14.112 2 Measurement period 2024: DOY 206–255; n = 7056, *all values in [m/s] Table 2 shows the WRE for each measured plot at different measurement heights. The WRE in 2023 was distributed in the following order: SS (1m MH) > SS (2m MH) > MS (2m MH) > MS (1m MH). In 2024, the WRE was distributed in the following order: SS (1m MH) = MS (1m MH) > MS (2m MH) > SS (2m MH). It is remarkable that the mean WRE was lower in 2023 than in 2024 at both measurement heights and for both crops. Therefore, the reason could be the generally lower mean WV in 2023 (Ø 2.0 m/s vs. 2024; Ø 2.2 m/s). Furthermore, the WRE in MS is almost the same as in the SS variant, despite the fact that the MS wind sensor was below the maize canopy during part of the growing season (in contrast to the SS sensor which remained to be above the soybean canopy throughout the seasons). This hints at a substantially strong wind flow through the 1.5 m wide maize strip canopy and an overall relatively homogeneous wind field, without considering wind direction effects. For a more detailed analysis, the WRE (during the 2023 and 2024 experimental seasons) is related to wind direction sectors, where we selected 8 wind sectors of 45° each, respectively, within the range of 0-360° (Fig. 10 a-d and Fig. 11 a-d). In theory, the wind directions perpendicular to the strip row orientation should cause the highest WRE. This is confirmed in 2023 (Fig. 10 a-d), where the highest daily WRE in all variants and measurement heights occurred in the sector 4 (136–180°), which corresponds to the SE wind direction, which was the dominating wind direction during the measurement period). The ranges from highest to the lowest WRE effects per wind sector of the measurement variants are as follows: Soybean strip (1m MH): 4 > 5 > 8 > 7 > 3 > 1 > 6 > 2; Soybean strip (2m MH): 4 > 3 > 7 > 6 > 8 > 5 > 2 > 1, Maize strip (1m MH): 4 > 7 > 6 > 8 > 3 > 1 > 2 > 5, and Maize strip (2m MH): 4 > 7 > 6 > 8 > 3 > 1 > 2 > 5. There is a good agreement of WRE per sector at (2m MH), (Fig. 10 a-d), where E-SE and W-NW show the highest effects in agreement with the main wind direction of the site. The results at (1m MH), (Fig. 11 a-d) are more diverse but also show the highest WRE in sector 4 (SE). At (1m MH) the other wind directions show, however, different behavior, which could be explained by channeling effects due to the two different canopy heights of maize and soybean (0.5m vs. 2m). That is, the wind sensor of the soybean strip was placed above the canopy, whereas the wind sensor of the maize strip was placed within the maize canopy, which led to a harmonization of WRE according to wind direction sectors. The lowest WRE, however, was found in all variants in the strip direction sectors 1–2 (N-NE) and sector 5 (south), supporting the importance of the strip direction in relation to the main wind direction for WRE. Although of an overall similar pattern than in 2023, the highest WRE effect was caused by the WNW wind direction in 2024 (Fig. 11 a-d). The highest daily WRE in the strip variants and measurement heights occurred in sector 7 (271–315°), which corresponds to WNW wind directions (perpendicular to the strip row orientation as well). The highest daily WRE in all variants and measurement heights occurred in sector 7 (270–315°), which corresponds to NW wind direction. The ranges from highest to the lowest WRE effects per wind sector of the measurement variants are as follows: Soybean strip (1m MH): 7 > 4 > 3 > 6 > 5; Soybean strip (2m MH): 7 > 3 > 4 > 5 > 6, Maize strip (1m MH): 7 > 3 > 6 > 4 > 5, and Maize strip (2m MH): 7 > 6 > 5 > 4 > 3. As in 2023, it is shown that the wind sectors from NW and SE (mainly the sectors 3, 4, 6, 7) caused the highest mean WRE, depending on the wind direction frequency during the two seasons. There is a congruence of WRE per sector in the measurement height of (2m MH ) (Fig. 11 a-d), where sector W-NW shows the highest effect in comparison with the main wind direction of the year 2024 (NW). The results at 1m MH; (Fig. 11 a-d) show the same tendency as the other results, the highest WRE occurs in WS 7 (NW). In 2024, WS classes 1, 2 and 8 did not occur at all during the measurement period. Precipitation based water use efficiency The development of precipitation based AGDB (above ground dry biomass) WUE followed a similar trend in both years. In 2023, a total of 34.0 t/ha was harvested for maize and soybean combined, with a total WUE of 276.5 kg/m³. In comparison, the WUE for the sole crops was 147.2 kg/m³ for maize and 56.2 kg/m³ for soybean. In 2024, 25.1 t/ha was achieved for both crops (maize and soybean), with a biomass WUE of 343.3 kg/m³ (Tables 3 and 4 ). For the sole crops the harvest was 18.0 t/ha for maize and 6.3 t/ha for soybean and the biomass WUE was 245.9 kg/m³ for maize and 86.7 kg/m³ for soybean. The lower WUE of both crops in 2024 can be attributed to the hotter and drier conditions during the main growing phase (June-July-August), which increased unproductive evaporation losses from the crop stand. Concerning the grain yield WUE a not the same trend can be shown. Comparing the intercropping parcels with the sole crop parcels, in 2023 the maize shows a deficit of (-22 kg/ m³) and the soybean a surplus of (2.8 kg/ m³). In 2024, comparing the intercropping parcels with the sole crop parcels the maize showed a deficit of (56.7 kg/ m³) and the soybean a deficit of (21.7 kg/ m³), (Tables 3 and 4 ). Table 3 Overview of wind velocity data (1 and 2m measuring height) for 2023, including mean values across replications (n = 18x10 3 ) and DOY range (171–283) in combination with the biomass data and yield data and the calculated biomass WUE of above ground dry matter (at harvest) and the calculated yield based WUE Plot Measurement height* Range** Mean** AGDM / Grain yield*** Biomass WUE / Grain yield WUE 2 MS 1 m 0.1–1.2 0.7 ± 0.2 - - MS 2 m 0.4–4.3 1.4 ± 0.8 25.9/ 6.5 205.3/ 51.3 SS 1 m 0.1–1.1 0.5 ± 0.6 - - SS 2 m 0.4–4.3 1.3 ± 1.0 8.1/ 1.7 71.1/ 15 MM 1m / 2m na na 18.6/ 9.7 147.2/ 77.1 SM 1m / 2m na na 6.4/ 2.6 56.2/ 22.7 *above ground level; n = 14.112; **wind velocity m/s; *** tons/hectare as average in all plot replications; 1 bare soil plot; 2 kg/m³. Table 4 Overview of wind velocity data (1 and 2m measuring height) for 2024, including mean values across replications (n = 19.3x10 3 ) and DOY range (206–255) in combination with the biomass data and yield data and the calculated biomass WUE of above ground dry matter (at harvest) and the calculated yield based WUE. The REF2 variant represents soybean monocrop plot. Plot Measurement height * Range** Mean** AGDM / grain yield*** Biomass WUE / Grain yield WUE 2 MS 1 m 0.2–2.1 0.7 ± 0.7 - - MS 2 m 0.5–3.2 1.4 ± 1.1 20.4 / 5.4 279.0 / 73.3 SS 1 m 0.1–2.2 0.5 ± 0.6 - - SS 2 m 0.8–3.9 1.3 ± 1.0 4.7 / 0.9 64.2 / 12.2 SM 1 1 m 0.3–7.3 0.9 ± 0-.7 - - SM 1 2 m 0.7–4.8 1.5 ± 1.2 6.3 / 2.5 245.9 / 33.9 MM na na na 18.0 / 9.5 130.0 / 86.7 *above ground level; n = 7056; **wind velocity m/s; *** tons/hectare as average in all plot replications; 1 soybean monocrop plot; 2 kg/m³. 4 Discussion To address the research questions regarding wind breaking effects within a maize–soybean strip intercrop system, field measurements were conducted over two growing seasons (2023 and 2024). Each season included approximately five months of continuous observations on experimental plots comprising both monocrop and 1.5 m width strip variants, with a row width of 50 cm within the parcels. The strip plots (60 m² per variant, with two replications) and eight replicates of the overall plots were used for above ground dry biomass (AGDB), yield, and microclimatic measurements, with selected plot variants designated for detailed assessments. The first research question focuses on how strip intercropping affects canopy wind velocities and water use efficiency and the second research question focuses on the associated wind reduction effects (WRE) in regard to strip orientation vs. wind direction. Our results indicate that the WRE is strongly influenced by specific 45° wind sectors, which correspond to the main climatic wind directions of the experimental site. In 2023 and 2024 maize and soybean growing periods, the main prevailing wind directions were similar but with different contributions, from west to north and from south to southwest, respectively. With the intercropping strips-oriented north–south, the highest wind reductions in 2023 were observed from the southeast and northwest sectors at both 1 m and 2 m measurement heights. In 2024 the predominant wind direction was higher from west–northwest, but maximum wind reductions were recorded from the southwest and northwest at 2m above ground level. The wind reduction in both observation years, expressed as a percentage, was between 6.6.-13.3% at the 2m measuring height on both plots relative to the dataset from the INCA grid. At 1m measuring height, the reduction was significantly higher in both crops, ranging between 24 and 44%. This result is comparable to other studies, which reported for example 6.7% wind reduction at 2m measuring height (Sun et al., 1994) for a similar strip intercropping design. Comparable reductions ranging from 17% to 67% have also been reported in studies focusing on the mitigation of wind erosion in connection with the reduction of wind velocities (van Ramshorst et al., 2022 ). A detailed analysis of wind velocity revealed significant variability. In 2023, at 2 m measuring height, the highest WV across all plots (INCA grid dataset; MS-maize strip; soybean strip (SS) occurred from south-east (181–270°). At 1 m measuring height, the dominant WS were 2 (91–180°) on the REF1 plot, 4 (270–360°) on the MS plot, and 2 on the SS plot. In contrast, in 2024, at 2 m measuring height, sector 4 (270–360°) showed the highest WV, while at 1 m measurement height, the dominant WS for both MS and SS was 3 (90–180°). Despite interannual differences attributable to varying weather conditions, wind reduction effects were consistently observed. Overall, the mean WRE in both years (2023–2024) was 0.6- 2.0 m/s at 2 m in the soybean strip and from 0.5 to 2.1 m/s in the maize strip, with elevated but similar ranges in 1m measuring height (1.0-1.2 in the soybean plot (SS) and 1.1–1.4 in the maize plot (MS)). When analyzed in 45° increments, the highest reductions at 2 m occurred in wind direction sectors 4 and 7 in 2023 and in sectors 7 and 6 in 2024, while at 1 m, sectors 4 and 3 (2023) and sectors 7 and 3 (2024) showed the greatest effects. Notably, the maximum reduction was observed in the soybean plot at 1 m in 2023 (1.9 m/s) and in the maize strip at 2 m in 2024 (4.9 m/s). With respect to AGDB WUE, an improvement of 28% (maize) and 21% soybean compared to sole crops was observed in 2023. Concerning the biomass WUE in 2024, the intercropping experiment had a surplus of 11.9% (maize) and a deficit of 26.1% (soybean). Furthermore, the grain yield WUE showed in 2023 a deficit of 30% (maize) and a surplus of 18.6% (soybean). In 2024 the yield- based WUE was generally negative in comparison to the yield in the sole crops (-43% (maize); -65% (soybean)). Nevertheless, a measurable advantage over sole crops was only soybean (+ 18.6%) in 2023 present. The results from 2023 are in good agreement with comparable studies, which reported an increased yield- based WUE of 25% and an improvement in biomass WUE of 24% in intercropping systems (Chen et al., 2018 , Qian et al., 2022 ). In another study (Mao Lilli et al., 2012), biomass WUE ranged from − 26.1% to + 28%, suggesting that intercropping, due to interactions within the strips, can also negatively affect yield (e.g. in our case study − 26 to -65%). This circumstance may explain the 2024 yield- based WUE results, where the advantage of intercropping over sole cropping was not present. Conclusions The challenge of increasing irrigation water demand and increasing water scarcity due to ongoing climate change in the study area underscores the importance of water conservation strategies. The design of our experiment focused on analyzing strip intercropping effects for wind protection measures in order to reduce non-productive evapotranspiration to improve WUE. Our findings confirm that the wind reduction effect in the soybean strip is robust across years, despite uncertainties in in-situ measurements. We found an overall positive effect on biomass production and related AGDB WUE of maize and soybean in the strip variants in our specific strip design with relatively small strip widths of 1.5m, however, with one exception for soybean in 2024. Obviously, biomass and grain yield WUE is not only affected by the wind breaking effect, but in combination with other factors, such as shading or other growth limitations, which may dominate under specific circumstances such as in more humid climates. This would need more research, e.g. considering different strip widths effects and row widths. The AGDB WUE was variable across the two observed years and in the maize/ soybean monocrop plots (MM, SM) generally lower than in the strip intercropped plots (MS, SS). Only in 2024, the soybean intercropping plots showed lower biomass WUE than the monocrop variants. Concerning the grain yield WUE, the comparison between monocrop and intercropping was not favorable for the intercropping, because it was only in 2023 in the intercropped soybean plot positive (+ 18.6%). Further research is needed additionally to assess the scalability of these results and integrating intercropping systems of different designs with wind protection. In the context of ongoing climate change new cropping systems will be crucial for sustainable agricultural management, especially in the face of rising temperatures, altered precipitation patterns, and increased yield variability in monocropping systems. Abbreviations WUE water use efficiency AT air temperature RH relative air humidity P precipitation WV wind velocity WRE wind reduction effect asl above sea level WMO World Meteorological Organisation MJJAS May-June-July-August-September LTM long term mean MAT mean annual temperature MH measuring height above ground level DOY day of year AGDM above ground dry matter at harvest REF1 bare soil plot (2023) REF2 soybean monocrop plot (2024) MM maize monocrop plot SM soybean monocrop plot SS soybean strip intercropping plot MS maize strip intercropping plot AGDB above ground dry biomass ARIS agricultural risk information system INCA Integrated Nowcasting through comprehensive Analysis Declarations Author Contribution Author contributionsJ.E. conceived and designed the study. G.G. and A.S. conducted data gathering and data processing. K.G. helped with statistical analysis in R and other analysis tools, H.W. and J.F. provided the research area, P.W. was part of the advisory board of the doctoral thesis from G.G., and G.G. and J.E. wrote the article. Acknowledgement The field experiment was carried out in the frame of the EU project InterCropValues, funded by the European Commission (EC). In general, I would like to dedicate this article to my father-in-law, Richard Retzl, and Jürgen Friedel; they sadly passed away during the preparation of this work. Special thanks are extended to Dr. Kerstin Michel for her valuable suggestions and unwavering support in all scientific matters. The authors also acknowledge the BOKU University for covering the submission fee for this open access article. References Aust, G., Leitgeb, E. (2024) eBOD2 Digitale Bodenkarte Österreichs. Hrsg: Bundesforschungs- und Ausbildungszentrum für Wald, Naturgefahren und Landschaft (BFW). https://bodenkarte.at, (accessed on 7th Februar 2024). Briggs, L. J., & Shantz, H. L. (1913). The water requirement of plants (No. 284-285). US Government Printing Office. Carslaw, D.C. und K. Ropkins, (2012). openair — an R package for air quality data analysis. Environmental Modelling & Software, Volume 27-28, pp. 52–61. https://doi.org/10.1016/j.envsoft.2011.09.008 Chen, H., Anshen, Q.,, Quiang, C., Yantai, G., und Zhadong, L. (2014) Quantification of Soil Water Competition and Compensation Using Soil Water Differences between Strips of Intercropping, 321-330. https://doi.org/10.1007/s40003-014-0134-6 Chen, G., Kong, X., Gan, Y., Zhang, R., Feng, F., Yu, A., ... & Chai, Q. (2018). Enhancing the systems productivity and water use efficiency through coordinated soil water sharing and compensation in strip-intercropping. Scientific Reports, 8(1), 10494. https://doi.org/10.1038/s41598-018-28612-6 Comtois, D. (2024). summarytools: Tools to Quickly and Neatly Summarize Data. R package version 1.0.1, https://CRAN.R-project.org/package=summarytools Eitzinger, J., Daneu, V., Kubu, G., Thaler, S., Trnka, M., Schaumberger, A., ... & Tran, T. M. A. (2024). Grid based monitoring and forecasting system of cropping conditions and risks by agrometeorological indicators in Austria–Agricultural Risk Information System ARIS. Climate Services, 34, 100478. https://doi.org/10.1016/j.cliser.2024.100478 Feng-yun, Z., Pu-te, W., Xi-ning, Z., & Xue-feng, C. (2012). Water-saving mechanisms of intercropping system in improving cropland water use efficiency. Yingyong Shengtai Xuebao, 23(5). Gao, Y., Duan, A., Qiu, X., Li, X., Pauline, U., Sun, J., & Wang, H. (2013). Modeling evapotranspiration in maize/soybean strip intercropping system with the evaporation and radiation interception by neighboring species model. Agricultural Water Management, 128, 110-119. https://doi.org/10.1016/j.agwat.2013.06.020 GeoSphere Austria Datenportal. (2024), https://data.hub.geosphere.at/, [accessed on 18th September, 2024 and accessed on 23th October, 2025 (INCA)] Haiden, T., Kann, A., Wittmann, C., Pistotnik, G., Bica, B., & Gruber, C. (2011). The Integrated Nowcasting through Comprehensive Analysis (INCA) system and its validation over the Eastern Alpine region. Weather and Forecasting, 26(2), 166-183. https://doi.org/10.1175/2010WAF2222451.1 INSPIRE (2024) INSPIRE Geodatenportal https://inspire-geoportal.ec.europa.eu/srv/ger/catalog.search#/home (accessed on 20th April 2024). IUSS Working Group WRB (2022)https://wrb.isric.org/documents/#:~:text=The%20correct%20citation%20is%3A%20IUSS,creating%20legends%20for%20soil%20maps (accessed on 2th Februar 2024). Kirchner,M., Strauss,F., Heumesser,C., und Schmid,E. (2012) Integrative model analysis of adaptation measures to a warmer and drier climate. Jahrbuch der Österreichischen Gesellschaft für Agrarökonomie (Österreichischen Gesellschaft für Agrarökonomie) 21(1), 177-186. Mao, L., Zhang, L., Li, W., van der Werf, W., Sun, J., Spiertz, H., & Li, L. (2012). Yield advantage and water saving in maize/pea intercrop. Field Crops Research, 138, 11-20. https://doi.org/10.1016/j.fcr.2012.09.019 Microsoft Corporation. (2023). Microsoft Excel (Version 16.80) [Software]. available: https://www.microsoft.com/ Neudorfer, W., & Weyermayer, H. (2007). Securing groundwater use and reestablishing the water balance by artificial recharge of groundwater in the region of Marchfeld, Austria. Water Practice and Technology, 2(3), wpt2007065. https://doi.org/10.2166/wpt.2007.065 Neugebauer, N., & Vuolo, F. (2014). Crop water requirements on regional level using remote sensing data—A case study in the Marchfeld Region. Photogramm. Fernerkund. Geoinformation, 2014, 369-381. Radke, J. K., & Hagstrom, R. T. (1976). Strip intercropping for wind protection. Multiple Cropping, 27, 201-222. Rahman, T., Ye, L., Liu, X., Iqbal, N., Du, J., Gao, R., ... & Yang, W. (2017a). Water use efficiency and water distribution response to different planting patterns in maize–soybean relay strip intercropping systems. Experimental Agriculture, 53(2), 159-177. https://doi.org/10.1017/S0014479716000260 Rahman, T., Liu, X., Hussain, S., Ahmed, S., Chen, G., Yang, F & Yang, W. (2017b). Water use efficiency and evapotranspiration in maize-soybean relay strip intercrop systems as affected by planting geometries. PloS one, 12(6), e0178332. https://doi.org/10.1371/journal.pone.0178332 Raseduzzaman, M. D. (2016). Intercropping for enhanced yield stability and food security. Department of Biosystems and Technology,. Master Thesis. https://www.researchgate.net/profile/Md-Raseduzzaman/publication/320402915_Intercropping_for_enhanced_yield_stability_and_food_security/links/59e22b10a6fdcc7154d80ce1/Intercropping-for-enhanced-yield-stability-and-food-security.pdf Raza, M. A., Gul, H., Wang, J., Yasin, H. S., Qin, R., Khalid, M. H. B., & Yang, W. (2021a). Land productivity and water use efficiency of maize-soybean strip intercropping systems in semi-arid areas: A case study in Punjab Province, Pakistan. Journal of Cleaner Production, 308, 127-282. https://doi.org/10.1016/j.jclepro.2021.127282 Raza, M. A., Yasin, H. S., Gul, H., Qin, R., Mohi Ud Din, A., Khalid, M. H. B., ... & Yang, W. (2022b). Maize/soybean strip intercropping produces higher crop yields and saves water under semi-arid conditions. Frontiers in Plant Science, 13, 1006720. https://doi.org/10.3389/fpls.2022.1006720 Revelle, W. (2024). psych: Procedures for Psychological, Psychometric, and Personality Research. Northwestern University, Evanston, Illinois. R package version 2.4.3, https://CRAN.R-project.org/package=psych RStudio Team. (2025). RStudio: Integrated Development Environment for R (Version 4.3.2) [Software]. Verfügbar unter https://www.rstudio.com/ Salehi, A., Mehdi, B., Fallah, S., Kaul, H. P., & Neugschwandtner, R. W. (2018a). Productivity and nutrient use efficiency with integrated fertilization of buckwheat–fenugreek intercrops. Nutrient cycling in agroecosystems, 110, 407-425. https://doi.org/10.1007/s10705-018-9906-x Salehi, A., Fallah, S., & Kaul, H. P. (2017b). Broiler litter and inorganic fertilizer effects on seed yield and productivity of buckwheat and fenugreek in row intercropping. Archives of Agronomy and Soil Science, 63(8), 1121-1136. https://doi.org/10.1080/03650340.2016.1258114 Strauss, F., Schmid, E., Moltchanova, E., Formayer, H., & Wang, X. (2012). Modeling climate change and biophysical impacts of crop production in the Austrian Marchfeld Region. Climatic Change, 111, 641-664. https://doi.org/10.1007/s10584-011-0171-0 Sun, D., & Dickinson, G. R. (1994). A case study of shelterbelt effect on potato (Solanum tuberosum) yield on the Atherton Tablelands in tropical north Australia. Agroforestry Systems, 25, 141-151. Thaler, S., Eitzinger, J., Dubrovský, M., & Trnka, M. (2008a). Climate change impacts on selected crops in Marchfeld, Eastern Austria. In 28th conference on agricultural and forest meteorology Vol. 28. Thaler, S., Eitzinger, J., Rischbeck, P., Dubrovsky, M., & Trnka, M. (2010b). Vulnerability of crops to climate change in Northeastern Austria. Bulgarian Journal of Meteorology and Hydrology, 15(1), 50-61. Thaler, S., Eitzinger, J., Trnka, M., & Dubrovsky, M. (2012c). Impacts of climate change and alternative adaptation options on winter wheat yield and water productivity in a dry climate in Central Europe. The Journal of Agricultural Science, 150(5), 537-555. https://doi.org/10.1017/S0021859612000093 Thinley, K., Sithup, K., Choden, T., Wangmo, P., Deki, S., Dema, T., ... & Katsura, K. (2024). Establishment of a high-yield intercropping system for maize and legumes under rainfed conditions in eastern Bhutan. Plant Production Science, 1-15. https://doi.org/10.1080/1343943X.2024.2354544 Qian, C. A. I., Zhan-xiang, S. U. N., Wen-bin, W. A. N. G., Wei, B. A. I., Gui-juan, D. U., Yue, Z. H. A. N. G., ... & Feng-yan, Z. H. A. O. (2022). Yield and water use of maize/soybean intercropping systems in semi-arid western Liaoning. Chinese Journal of Agrometeorology, 43(07), 551. 10.3969/j.issn.1000-6362.2022.07.004 van Ramshorst, J. G., Siebicke, L., Baumeister, M., Moyano, F. E., Knohl, A., & Markwitz, C. (2022). Reducing wind erosion through agroforestry: a case study using large eddy simulations. Sustainability, 14(20), 13372. https://doi.org/10.3390/su142013372 Wallace, J. S. (2000). Increasing agricultural water use efficiency to meet future food production. Agriculture, ecosystems & environment, 82(1-3), 105-119. https://doi.org/j.issn.1000-6362.2022.07.004 Willey, R. W. (1990). Resource use in intercropping systems. Agricultural water management, 17(1-3), 215-231. https://doi.org/10.1016/0378-3774(90)90069-B Water, U. N. (2024). Progress on change in water-use efficiency. https://doi.org/10.4060/cd2023en Taiyun Wei and Viliam Simko (2021). R package 'corrplot': Visualization of a Correlation Matrix. (Version 0.92). https://github.com/taiyun/corrplot Yin, W., Chai, Q., Zhao, C., Yu, A., Fan, Z., Hu, F., ... & Coulter, J. A. (2020). Water utilization in intercropping: A review. Agricultural Water Management, 241, 106335. https://doi.org/10.1016/j.agwat.2020.106335 Zhao, Y., Fan, Z., Hu, F., Yin, W., Zhao, C., Yu, A., & Chai, Q. (2019). Source-to-sink translocation of carbon and nitrogen is regulated by fertilization and plant population in maize-pea intercropping. Frontiers in Plant Science, 10, 891. https://doi.org/10.3389/fpls.2019.00891 Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":189306,"visible":true,"origin":"","legend":"\u003cp\u003eLong-term data series showing the yearly precipitation sum, precipitation during MJJAS, mean annual temperature, and mean annual temperature during MJJAS over the period 2003-2024 (source: GEOSPHERE Austria).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8059550/v1/08d5e7445b2aefdb0cc54bb6.png"},{"id":96400229,"identity":"9d836051-3c9d-413e-92f1-4ed3f21edbb5","added_by":"auto","created_at":"2025-11-20 16:04:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":126127,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly precipitation sums compared with the monthly mean annual temperatures (2023-2024), highlighted bar shows the assumed growing period and the monthly precipitation (MJJAS); orange line shows the LTM 2003-2024 in temperature; blue bars show the LTM 2003-2024 in precipitation; black and white bars show the monthly precipitation sum (2023/ 2024); dotted lines show the monthly mean temperatures (2023-2024)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8059550/v1/2ca61ccf67cb4e110e4a6aa6.png"},{"id":96454224,"identity":"ae29f0d5-d79e-4ad6-bbdb-277497b25fb6","added_by":"auto","created_at":"2025-11-21 10:02:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":850968,"visible":true,"origin":"","legend":"\u003cp\u003eLeft: Soil profile on the strip intercropping trial field in Raasdorf; Middle: Schematic soil profile from the Agricultural Soil Map EBOD2 (2023); Right: Soil profile with installed soil moisture and soil temperature sensors.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8059550/v1/05e8acb75f41049826a88a50.png"},{"id":96400234,"identity":"28b71c80-f105-4d64-ae2d-758363a6d7a2","added_by":"auto","created_at":"2025-11-20 16:04:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":620756,"visible":true,"origin":"","legend":"\u003cp\u003ea-c: Overview of the strip intercropping field experiment in Raasdorf: a) wind rose showing the distribution of the wind directions and the wind velocities in 2023 during the observed period; b) wind rose showing the distribution of wind direction and wind velocities in 2024 during the observed period; c) year 2023 plot design and overview of the observed plots (REF, MS, SS). The measurements in 2023 were carried out in row B1 (two bare soil plots (REF)) and row B4 (subplot (T3)). The bare soil plots were used to validate the INCA datasets.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8059550/v1/856862976bb5fe25e7bf78e8.png"},{"id":96454265,"identity":"44306cb3-a797-4eaf-8811-5d608a1cc809","added_by":"auto","created_at":"2025-11-21 10:02:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":501571,"visible":true,"origin":"","legend":"\u003cp\u003ea-d: Details of the strip intercropping field experiment in Raasdorf (2023): a) image of the REF plot (bare soil plots) with installed sensors; b) plot design and location of the sensors in the REF plot; c) image of the SS plot with installed sensors; d) plot design and locations of the sensors in the SS plot.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8059550/v1/212883bbecea4dc4d2b6fb28.png"},{"id":96453163,"identity":"6c31ce4b-f3ea-459d-93f5-096949d07b5f","added_by":"auto","created_at":"2025-11-21 09:58:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":246105,"visible":true,"origin":"","legend":"\u003cp\u003ePlant height development in both observation years (2023-2024) on the soybean (SS) and maize (MS) strips in the intercropped plots, connected with the BBCH Status of the plants and the day of the year when the height measurement was done.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8059550/v1/612e1432814c6513691428f0.png"},{"id":96400243,"identity":"6ec230db-da8d-4610-88a2-d4e1bdb2b9d8","added_by":"auto","created_at":"2025-11-20 16:04:21","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":135332,"visible":true,"origin":"","legend":"\u003cp\u003eWind direction distributions (8 wind sectors; 0-45°, 46-90°, 91-135°, 136-180°, 181-225°, 226-270°, 271-315°, 316-360°) for 2023 and 2024 (data source: Geosphere, 2024), based on daily mean wind directions\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8059550/v1/5a18d21dad7977acd4de59c9.png"},{"id":96400236,"identity":"75098582-30e1-4b07-931a-6a812411a9de","added_by":"auto","created_at":"2025-11-20 16:04:21","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":96253,"visible":true,"origin":"","legend":"\u003cp\u003ea-f: Comparison of the daily mean wind velocities in different measuring heights of each observed plot (REF1-bare soil, MS, SS) over the entire measurement period in 2023, combined with different wind sectors: a) wind velocities on the REF plot at 2 m MH; b) wind velocities on the MS plot at 2 m MH; c) wind velocities on the SS plot at 2 m MH; d) wind velocities on the REF plot at 1 m MH; e) wind velocities on the MS plot at 1 m MH; f) wind velocities on the SS plot at 1 m MH. The wind directions are divided into 4 sectors: Sector 1 (0-90°), sector 2 (91-180°), sector 3 (181-270°), and sector 4 (271-360°). These sectors correspond to the main geographic directions. (DOY 217-270); n = 7776; gray dots represent outliers.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8059550/v1/0619a097a2bab56a7a80db94.png"},{"id":96453931,"identity":"5823776e-c8ca-446d-866f-1233a70a3bf8","added_by":"auto","created_at":"2025-11-21 10:02:06","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":96294,"visible":true,"origin":"","legend":"\u003cp\u003ea-f: Comparison of the daily mean wind velocities at different measuring heights for each observed plot (REF- soybean monocrop, MS, SS) over the entire measurement period in 2024, combined with different wind sectors: a) wind velocities on the REF2 plot at 2 m MH; b) wind velocities on the MS plot at 2 m MH; c) wind velocities on the SS plot at 2 m MH; d) wind velocities on the REF plot at 1 m MH; e) wind velocities on the MS plot at 1 m MH; f) wind velocities on the SS plot at 1 m MH. The wind directions are divided into 4 sectors: Sector 1 (0-90°), sector 2 (91-180°), sector 3 (181-270°), and sector 4 (271-360°). These sectors correspond to the main geographic directions. (DOY 217-270); n = 7776; Gray dots represent outliers.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-8059550/v1/70818833457229cb1327aae0.png"},{"id":96453888,"identity":"0cfa2507-8c2f-4471-a14a-256959b29767","added_by":"auto","created_at":"2025-11-21 10:02:02","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":101919,"visible":true,"origin":"","legend":"\u003cp\u003ea-d: Comparison of the wind reduction effect in 2023\u003cstrong\u003e,\u003c/strong\u003e based on wind velocities at different measuring heights for each observed plot (MS, SS) over the entire measurement period in2023, combined with the different wind sectors(1-8): a) wind reduction effect on the SS plot at 2 m MH; b) wind reduction effect on the MS plot at 2 m MH; c) wind reduction effect on the SS plot at 1 m MH; d) wind reduction effect on the MS plot at 1 m MH. The wind directions are divided into 8 sectors: Sector 1 (0-45°), sector 2 (46-90°), sector 3 (91-135°), sector 4 (136-180°), sector 5 (181-225°), sector 6 (226-270°), sector 7 (271-315°), and sector 8 (316-360°). These sectors correspond to the main geographic directions (N, NE, E, SE, S, SW, W, NW). (DOY 217-270); n = 7776.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-8059550/v1/1a93d7a6abef3c5826d1e8dc.png"},{"id":96400241,"identity":"36f0d4e5-0a95-4e59-937a-f07cdd16b377","added_by":"auto","created_at":"2025-11-20 16:04:21","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":93478,"visible":true,"origin":"","legend":"\u003cp\u003ea-d: Comparison of the wind reduction effect in 2024, based on wind velocities at different measuring heights for each observed plot (MS, SS) over the entire measurement period in 2024\u003cstrong\u003e \u003c/strong\u003ein combination with the different wind sectors(1-8): a) wind reduction effect on the SS plot at 2 m MH; b) wind reduction effect on the MS plot at 2 m MH; c) wind reduction effect on the SS plot at 1 m MH; d) wind reduction effect on the MS plot at 1 m MH. The wind directions are divided into 8 sectors: Sector 1 (0-45°), sector 2 (46-90°), sector 3 (91-135°), sector 4 (136-180°), sector 5 (181-225°), sector 6 (226-270°), sector 7 (271-315°), and sector 8 (316-360°). These sectors correspond to the main geographic directions (N, NE, E, SE, S, SW, W, NW). (DOY 217-270); n = 7776\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-8059550/v1/1483b6a5d76dffe3aacf22ad.png"},{"id":96456891,"identity":"12204433-34cb-413c-af5c-2578760d2dc4","added_by":"auto","created_at":"2025-11-21 10:08:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3793120,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8059550/v1/b667eb96-bda0-4c1b-b424-fac448006f94.pdf"},{"id":96400228,"identity":"9eb67692-3d3f-41fa-9fc8-1d8011b991e8","added_by":"auto","created_at":"2025-11-20 16:04:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":31567,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-8059550/v1/774051cbae254f8ff0c1475f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of a maize- soybean strip intercropping system on canopy level wind velocities in a semi-humid region of Central Europe","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eWater is essential for plant growth and yield formation and represents a limiting factor, especially in arid and semi-arid regions (Gao et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In the context of climate change, water scarcity is expected to increase, particularly in these regions (Rahman et al., 2017). Limited water availability is also one of the main factors contributing to declining agricultural crop productivity (Feng-Yun et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Global climate change is projected to result in higher evapotranspiration rates, reduced precipitation, and a greater frequency of extreme rainfall events (Rahman et al., 2017). These adverse effects may push agricultural production in many regions closer to critical thresholds (Rasedzuzzaman et al., 2016). Therefore, improving WUE or biomass / grain yield based WUE through adapted crop management practices has become a key strategy to ensure sustainable use of natural resources, including the implementation of intercropping systems (Zhao et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Intercropping\u0026mdash;the simultaneous cultivation of two or more crop species on the same field\u0026mdash;allows for complementary use of water and nutrients in both space and time (Salehi et al., 2018; 2017) and has been shown to increase land productivity (Gao et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Willey, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Sustainable improvements in WUE and land productivity are therefore central objectives in modern agricultural strategies (Wallace, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; UN, 2024), particularly in the face of increasing competition for water resources (Raza et al., 2022). Synergistic effects in water use, such as reduced competition and enhanced compensation mechanisms, are essential for the sustainability of intercropping systems in water-limited environments (Chen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Previous studies have shown varying effects of intercropping on WUE (Mao et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). One specific form, strip intercropping of two crop types of different canopy height, offers the additional benefit of wind protection for the lower height crop, while enabling more flexible field operations compared to permanent windbreaks (Radke \u0026amp; Hagstrom, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1976\u003c/span\u003e). Furthermore, the geometry of strip intercropping can significantly influence WUE, either positively or negatively, compared to sole cropping systems (Rahman et al., 2017, 2017). Optimized strip intercropping designs have been reported to save 20\u0026ndash;50% of water (or land equivalent), especially under limited soil water availability and increasing drought risk due to climate change (Raza et al., 2022). Recent findings suggest that maize\u0026ndash;soybean strip intercropping improves both water and nitrogen use efficiency and enhances land productivity under irrigated conditions (Raza et al., 2021). Similar benefits have been observed under rainfed conditions in arid and semi-arid regions, leading to more stable yields and higher WUE (Yin et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mao et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Integrating legumes into maize rows\u0026mdash;either in-row or as strips\u0026mdash;has also shown promise for improving productivity for smallholder farmers under rainfed conditions (Thinley et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIncreased frequency and severity of agricultural droughts as well as heat waves are expected under future climate scenarios, including Central Europe. This trend is particularly relevant in the Marchfeld region near Vienna\u0026mdash;one of Austria\u0026rsquo;s eight major agricultural production areas (Strauss et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Thaler et al., 2008; INSPIRE, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Future projections indicate a growing demand for irrigation in this region (Kirchner et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), raising concerns about groundwater depletion (Neudorfer \u0026amp; Weyermayer, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Consequently, research attention has shifted towards assessing water consumption and WUE in field crop systems within Austria\u0026rsquo;s drought-prone regions (Thaler et al., 2010; 2012; Neugebauer et al., 2014).\u003c/p\u003e\u003cp\u003eGiven the predicted changes in climatic conditions and their effects on crop\u0026ndash;soil water balance, the question arises whether adapted cropping systems such as strip intercropping could positively influence microclimatic conditions (i.e. WV, WUE) and thus improve the crop\u0026ndash;soil water balance. In light of increasing agricultural drought in Eastern Austria, particularly in the Marchfeld region with its relatively high wind loads, a two-year field experiment was conducted to address the following three main research questions: a) To what extent, and in what way, does a specific strip intercropping design influence wind velocities within the windbreaking range of the strips at canopy level? b) How crop water use efficiency (biomass and grain yield based) of the both strip crops (soybean and maize) is affected? c) How wind reduction effects are related to wind direction over two growing seasons?\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cp\u003eSite characteristics\u003c/p\u003e\n\u003cp\u003eThe strip intercropping field experiment was conducted in the Marchfeld region, a major agricultural production area near Vienna, Austria. The experimental site is located southwest of Raasdorf (48\u0026deg; 13\u0026prime; 58.98\u0026Prime; N, 16\u0026deg; 35\u0026prime; 30.984\u0026Prime; E) at an altitude of 156 m above sea level (asl) and has a flat exposition (0\u0026deg; slope). A hedgerow, approximately 4 m in height, was situated about 30 m north of the experimental field. This location in the Marchfeld region is characterized by its continental climate, typically experiencing hot, dry summers and cold winters with minimal snowfall, making it an ideal setting for studying agricultural adaptations to drought-prone conditions.\u003c/p\u003e\n\u003cp\u003eClimate characteristics\u003c/p\u003e\n\u003cp\u003eClimate characteristics were derived from data series of a nearby World Meteorological Organization (WMO)- standard weather station in Gro\u0026szlig;-Enzersdorf (source: Geosphere Austria, 2023 and 2024). For the maize and soybean growing season May- June-July-August- September (MJJAS), the precipitation sum in 2023 was 323 mm, which was equal to the long-term mean (LTM) of the period 2003\u0026ndash;2024 with 323 mm. The total precipitation in 2023 was 685 mm and showed an increase of 148 mm compared to the LTM of 537 mm. The mean annual temperature (MAT) for 2023 was 12.3\u0026deg;C, which was 1.3\u0026deg;C above the LTM (2003\u0026ndash;2024) of 11\u0026deg;C. In comparison, the MJJAS precipitation sum in 2024 of 450 mm was well above the LTM of the period 2003\u0026ndash;2024 (+\u0026thinsp;127 mm), caused primarily by a heavy precipitation event in early September at the end of the crop growing season. The total precipitation in 2024 was 648 mm and showed an increase of 111 mm compared to the LTM (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe MAT in 2023 of 12.7\u0026deg;C was 1.7\u0026deg;C above the LTM (2003\u0026ndash;2024), while the MAT in 2024 of 10.9\u0026deg;C was 0.1\u0026deg;C below the LTM (2003\u0026ndash;2024). The MJJAS mean temperature in 2023 was 19.7\u0026deg;C and was 2.5\u0026deg;C higher compared to the LTM (2003\u0026ndash;2023). In comparison, in 2024, the MJJAS mean temperature was 18.5\u0026deg;C and was 1.3\u0026deg;C higher than the LTM temperature (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eOver the growing season (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), the precipitation in June 2023 was 34 mm above the LTM (2003\u0026ndash;2024). In July, the precipitation amount was 45 mm below the LTM. In August, it was 31 mm above the LTM and showed a deficit of 36 mm in September. October also recorded an increase of 12 mm compared to the LTM. In the growing season of 2024, the precipitation in June was 21 mm above the LTM (2003\u0026ndash;2024). In July, the precipitation amount was 40 mm below the LTM. In August, it was also 17 mm below the LTM, and in September, due to an extremely heavy precipitation event, a surplus of 155 mm above the LTM occurred. October 2024 showed a deficit of 4 mm compared to the LTM (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eSoil characteristics\u003c/p\u003e\n\u003cp\u003eAccording to the WRB classification (IUSS Working Group WRB, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), the soil type can be classified as Chernozem, composed of calcareous fine sediments (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.bodenkarte.at\u003c/span\u003e\u003c/span\u003e; EBOD2; Aust et al., 2023). This soil type is well-supplied with water, has a good water storage capacity, and exhibits moderate permeability. The crop-available soil water capacity from 0\u0026ndash;1 m soil depth ranges between 140\u0026ndash;220 mm. The horizons of this soil type are predominantly A1/A2, sometimes A3, with a final C horizon. The following soil characteristics describe the investigated soil profile: clayey silt (Ul; 44%), loamy sand (Ls;16%), or sandy loam (Sl). The C horizons contain either Lt or sand (S; 40%) as their soil characteristics. The soil is calcareous (7.5 pH in 0.01m CaCl2) and easy to cultivate, making it high-quality farmland (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eExperimental plot locations and treatments\u003c/p\u003e\n\u003cp\u003eThe field trial (42m x 46m) in 2023 with a total area of 0.19 hectares (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec), consisted of 12 maize monocrop plots, 8 soybean monocrop plots, and 8 maize-soybean strip plots, all with the same size of 60 m2. The intercropping design consisted of 28 plots, arranged in four replications. Each strip plot included alternating strips of soybean and maize, with each strip being 1.5 meters wide and a row width of 50 cm, while the orientation of the strips was from north to south. This setup enabled a systematic comparison between the intercropped system and monoculture plots within the experimental layout.\u003c/p\u003e\n\u003cp\u003eThe locations of the plots (T1-T7) within the block (B1-B4) were randomized. The field trial in 2024 had almost the same design (total area of 0,16 hectares), consisting of 12 maize monocrop plots, 4 soybean monocrop plots, and 12 maize-soybean strip plots of 60 m2 each, but with a different (randomized) distribution of plots. Compared to 2023, the location of the field trial in 2024 was shifted 45 m south within the same field but keeping the same intercrop plot design.\u003c/p\u003e\n\u003cp\u003eThe sowing density (maize and soybean) was for both year equal (35\u0026ndash;55 seed grain/ m\u0026sup2;).\u003c/p\u003e\n\u003cp\u003eThe WV and WD within the measurements periods of both experimental years are shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea (2023) and Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb (2024), confirming the main WD from north-west and south-east, with higher mean wind velocities occurring in the 2024 measurement period.\u003c/p\u003e\n\u003cp\u003eIn both years the daily mean WV from the Integrated Nowcasting through Comprehensive Analysis (INCA) grid data (Haiden et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e), hosted by Geosphere Austria, of the experimental site (the spatial explicit 1 km\u003csup\u003e2\u003c/sup\u003e grid) were used as the reference. For example, this dataset is used, among others, the Agricultural Risk Information System (ARIS) for crop water balance monitoring (Eitzinger et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). In addition, the INCA dataset was validated with the local reference stations REF 1 and REF 2 in both measurement periods. The INCA dataset represents WMO standard surface conditions (open area, short grass) of the wind profile.\u003c/p\u003e\n\u003cp\u003eIn 2023, sowing of both crops (maize and soybean) was carried out on June 20th (delayed due to a crop failure at the first sowing attempt), and the microclimatic measurements in the selected plots (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec,d) started on August 5th, 2023 (DOY 217). The microclimatic measurements for the validation of the INCA grid dataset (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea, b) at the reference plot (REF1) began after a first sowing attempt on May 24th, 2023 (DOY 145) and were placed in the middle of the plots or strips. Soybean was harvested on 11th October and maize on 23rd October. The delayed sowing and harvesting date in 2023 were caused by extreme (cold and wet) weather conditions, which has to be considered as an unusual and rare cropping period in practice.\u003c/p\u003e\n\u003cp\u003eIn 2024, all microclimatic measurements started on June 1st, immediately after sowing of both crops simultaneously (30th April). In the second experimental year 2024, a similar measurement setup with the same sensor equipment as in the first year was applied, maintaining the same design measurement height above ground level (MH) of microclimatic variables and for WV measurements to compare two years of different weather conditions. In the second year, the reference weather station was placed in the center of a soybean monocrop plot within the strip intercropping experiment. Both crops were harvested on 11th September.\u003c/p\u003e\n\u003cp\u003eField measurements and microclimatic variables\u003c/p\u003e\n\u003cp\u003eIn 2023, the experimental setup included 6 agrometeorological weather stations (W1- W6), (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea-d). For 2024, the weather stations setup remained the same. Details of the data acquisition equipment, including type of data logger, specific sampling interval for each sensor, and logging intervals are listed in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e (\u003cspan class=\"InternalRef\"\u003eAppendix\u003c/span\u003e). The measured variables at each location within the field trial are listed in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e (\u003cspan class=\"InternalRef\"\u003eAppendix\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eCrop development and above ground dry biomass\u003c/p\u003e\n\u003cp\u003eSowing of maize and soybean in the strip intercropping experiment was conducted on the same day each year, although the sowing dates were differed between the two experimental years (June 20, 2023, and April 30, 2024, for both crops, respectively). In the observed years, plant heights were measured at five observation dates: Day of the year (DOY) 185\u0026ndash;283 in 2023 and DOY 206\u0026ndash;255 in 2024). The development of the plant height (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e) of both crops (maize and soybean) in 2023 was accelerated due to delayed sowing. To determine the above ground dry biomass (AGDB) and grain yield (at harvest) of both crops, samples were taken and related to the cropped area in tons per hectare. In addition, the growth stages of both crops were taxed by the BBCH phenological status to get information about the growth process over the two observed years.\u003c/p\u003e\n\u003cp\u003ePrecipitation based water use efficiency\u003c/p\u003e\n\u003cp\u003eThe AGDB (above ground dry biomass) WUE based on precipitation was calculated using the following Eq.\u0026nbsp;1, (Briggs et al., 1913):\u003c/p\u003e\n\u003cp\u003eAGDB WUE = (AGDB [t/ha]) / (precipitation [mm]) (1)\u003c/p\u003e\n\u003cp\u003eThe grain yield based WUE was calculated using the following Eq.\u0026nbsp;2, /Briggs et al., 1913):\u003c/p\u003e\n\u003cp\u003eGrain yield based WUE = (grain yield [t/ha]/ (precipitation [mm]) (2)\u003c/p\u003e\n\u003cp\u003eFor the calculation of the WUE in both years (2023\u0026ndash;2024) the differing reference data sets were used. As for 2023 the both strip crops growth period was from (DOY 185\u0026ndash;283) and in 2024 from DOY 206\u0026ndash;255 the results are not directly comparable in respect to a \u0026ldquo;theoretical\u0026rdquo; same seasonal crop growth period (which is, however, in practice variable as well from year to year due to changing annual weather conditions ). In our case there was no irrigation within the experimental field and so the measured precipitation for the calculation was used.\u003c/p\u003e\n\u003cp\u003eStatistical analysis tools\u003c/p\u003e\n\u003cp\u003eThe main statistical analysis was done by EXCEL (2023) sheets (. The logged data from the ZL6 data loggers was processed with the standard functions in EXCEL (minimum, maximum, mean, standard deviation). The gap filling and pre- analysis were also done by EXCEL. Concerning the wind roses and the processing of the other datasets the following r- packages were used: \u0026ldquo;openair\u0026rdquo;, \u0026ldquo;psych\u0026rdquo;, \u0026ldquo;summary tools\u0026rdquo;, \u0026ldquo;corrplot\u0026rdquo;, (Carslaw\u0026amp;Hopkins, 2012), (Revelle, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), (Comtois, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), (Wei\u0026amp;Simko, 2021). The packages were integrated within R-studio 4.3.2, (R-studio team, 2024).\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003eWind velocities within the strip intercropping variants\u003c/p\u003e\u003cp\u003eWD and WV in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e1\u003c/span\u003e compares the measurement results of WV at 1m and 2m measurement height (MH) and the statistical values of the different variants, i.e., the reference dataset (INCA), the soybean strip (SS) and maize strip (MS The data from the WV measurements during the field experiment of both years (2023, 2024) were compared. In addition, the WV data were divided into 4 WS to get an overview of the distribution of the WV for each sector.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOverview of the wind velocity statistics (range, mean, standard deviation (\u0026plusmn;) based on 10- minute measurements for the reference dataset (INCA) and each investigated plot (SS (soybean strip) and MS (maize strip) in measurement height (1 m, 2 m)).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlot\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003cp\u003e2023\u003csup\u003e1\u003c/sup\u003e*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003cp\u003e2024\u003csup\u003e2\u003c/sup\u003e*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003cp\u003e2023\u003csup\u003e1\u003c/sup\u003e*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003cp\u003e2024\u003csup\u003e2\u003c/sup\u003e*\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINCA dataset (1m MH)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.6\u0026ndash;4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6\u0026ndash;5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINCA dataset (2m MH)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.7\u0026ndash;5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7\u0026ndash;6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoybean strip plot (1 m MH)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1\u0026ndash;0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2\u0026ndash;2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoybean strip plot (2 m MH)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.4\u0026ndash;4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5\u0026ndash;3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaize strip plot (1 m MH)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2\u0026ndash;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.1\u0026ndash;4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaize strip plot (2 m MH)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.4\u0026ndash;4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5\u0026ndash;3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Measurement period 2023: DOY 217\u0026ndash;283; n\u0026thinsp;=\u0026thinsp;14.112\u003c/p\u003e\u003cp\u003e\u003csup\u003e2\u003c/sup\u003e Measurement period 2024: DOY 206\u0026ndash;255; n\u0026thinsp;=\u0026thinsp;7056, *all values in [m/s]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the observation year 2023, the WV differences between the MS plot and the INCA dataset were 0.7 m/s at 2m MH and 1.3 m/s at 1m MH. In the observation year 2024, the results were comparable: on the MS plot, the WV was 0.7 m/s at (2m MH) and 1.2 m/s at (1m MH). The mean in 1m measuring height (MH) shows a large deviation (mean) in the intercropping plots. The results from 2023 on the SS plot were 1.5 m/s at (1m MH) and 0.8 m/s at (2m MH). In 2024, the WV on the SS plot was 1.2 m/s at (1m MH) and 0.7 m/s at (2m MH) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Comparing the results of the mean WV in both observation years (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the INCA dataset show the highest mean WV at (2m MH) as well as at (1m MH) as expected.\u003c/p\u003e\u003cp\u003eFor comparison, the main wind directions during the years 2021\u0026ndash;2024 were in the NW-sector (29%), West (20%) and SE-sector (16%) according to measurements at 10m height at the representative Geosphere Austria weather station in \u0026ldquo;Gro\u0026szlig;-Enzersdorf\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure 9a-f: Comparison of the daily mean wind velocities at different measuring heights for each observed plot (REF- soybean monocrop, MS, SS) over the entire measurement period in 2024, combined with different wind sectors: a) wind velocities on the REF2 plot at 2 m MH; b) wind velocities on the MS plot at 2 m MH; c) wind velocities on the SS plot at 2 m MH; d) wind velocities on the REF plot at 1 m MH; e) wind velocities on the MS plot at 1 m MH; f) wind velocities on the SS plot at 1 m MH. The wind directions are divided into 4 sectors: Sector 1 (0\u0026ndash;90\u0026deg;), sector 2 (91\u0026ndash;180\u0026deg;), sector 3 (181\u0026ndash;270\u0026deg;), and sector 4 (271\u0026ndash;360\u0026deg;). These sectors correspond to the main geographic directions. (DOY 217\u0026ndash;270); n\u0026thinsp;=\u0026thinsp;7776; Gray dots represent outliers.\u003c/p\u003e\u003cp\u003eIn 2023, at 2m MH, the order of the WV was 4\u0026thinsp;\u0026gt;\u0026thinsp;3\u0026thinsp;\u0026gt;\u0026thinsp;2\u0026thinsp;\u0026gt;\u0026thinsp;1 within the INCA grid dataset, 3\u0026thinsp;\u0026gt;\u0026thinsp;4\u0026thinsp;\u0026gt;\u0026thinsp;2\u0026thinsp;\u0026gt;\u0026thinsp;1 on the MS plot, and 3\u0026thinsp;\u0026gt;\u0026thinsp;4\u0026thinsp;\u0026gt;\u0026thinsp;2\u0026thinsp;\u0026gt;\u0026thinsp;1 on the SS plot. At 1m MH, the distribution was 2\u0026thinsp;\u0026gt;\u0026thinsp;4\u0026thinsp;\u0026gt;\u0026thinsp;3\u0026thinsp;\u0026gt;\u0026thinsp;1 on the REF plot, 2\u0026thinsp;\u0026gt;\u0026thinsp;4\u0026thinsp;\u0026gt;\u0026thinsp;3\u0026thinsp;\u0026gt;\u0026thinsp;1 on the MS plot, and 2\u0026thinsp;\u0026gt;\u0026thinsp;4\u0026thinsp;\u0026gt;\u0026thinsp;3\u0026thinsp;\u0026gt;\u0026thinsp;1 on the SS plot. The lowest WV occurred across all MH and plots in WS 1 (wind from north-east).\u003c/p\u003e\u003cp\u003eIn 2024, at 2m MH, the order of WV was 4\u0026thinsp;\u0026gt;\u0026thinsp;2\u0026thinsp;\u0026gt;\u0026thinsp;3\u0026thinsp;\u0026gt;\u0026thinsp;1 within the INCA grid dataset, 4\u0026thinsp;\u0026gt;\u0026thinsp;3\u0026thinsp;\u0026gt;\u0026thinsp;2\u0026thinsp;\u0026gt;\u0026thinsp;1 on the MS plot, and 4\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026thinsp;\u0026gt;\u0026thinsp;3\u0026thinsp;\u0026gt;\u0026thinsp;2 on the SS plot. At 1m MH, the order was 3\u0026thinsp;\u0026gt;\u0026thinsp;4\u0026thinsp;\u0026gt;\u0026thinsp;2\u0026thinsp;\u0026gt;\u0026thinsp;1 on the REF plot, 3\u0026thinsp;\u0026gt;\u0026thinsp;2\u0026thinsp;\u0026gt;\u0026thinsp;4\u0026thinsp;\u0026gt;\u0026thinsp;1 on the MS plot, and 4\u0026thinsp;\u0026gt;\u0026thinsp;3\u0026thinsp;\u0026gt;\u0026thinsp;2\u0026thinsp;\u0026gt;\u0026thinsp;1 on the SS plot. In 2024, the lowest WV also occurred in the WS, 1, except for the SS plot at (2m MH), where the dominating WS was 2.\u003c/p\u003e\u003cp\u003eWind reduction effect within the strip intercropping variants\u003c/p\u003e\u003cp\u003eTo determine WRE, the WV measured on the REF plots were compared to the WV measured within the strips of MS and SS plots (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2\u003c/span\u003e). WRE (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were observed in all strip variants and measurement heights with respect to the monocrop plots (MS; SS), with distinct differences between the four WS, (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003ea-f and Fig.\u0026nbsp;9a-f).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOverview of the wind reduction effect for the intercropping plots (MS, SS) compared to the reference dataset (INCA) for the 2023 and 2024 experimental seasons at 1 m and 2 m wind measurement heights (based on Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlot\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003cp\u003e2023\u003csup\u003e1\u003c/sup\u003e*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003cp\u003e2024\u003csup\u003e2\u003c/sup\u003e*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003cp\u003e2023\u003csup\u003e1\u003c/sup\u003e*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003cp\u003e2024\u003csup\u003e2\u003c/sup\u003e*\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoybean strip plot (1 m MH)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0-4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0-3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoybean Strip plot (2 m MH)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0-2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026ndash;2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaize strip plot (1 m MH)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.4-4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0-3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaize strip plot (2 m MH)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0-2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0-2.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Measurement period 2023: DOY 217\u0026ndash;283; n\u0026thinsp;=\u0026thinsp;14.112\u003c/p\u003e\u003cp\u003e\u003csup\u003e2\u003c/sup\u003e Measurement period 2024: DOY 206\u0026ndash;255; n\u0026thinsp;=\u0026thinsp;7056, *all values in [m/s]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the WRE for each measured plot at different measurement heights. The WRE in 2023 was distributed in the following order: SS (1m MH)\u0026thinsp;\u0026gt;\u0026thinsp;SS (2m MH)\u0026thinsp;\u0026gt;\u0026thinsp;MS (2m MH)\u0026thinsp;\u0026gt;\u0026thinsp;MS (1m MH). In 2024, the WRE was distributed in the following order: SS (1m MH)\u0026thinsp;=\u0026thinsp;MS (1m MH)\u0026thinsp;\u0026gt;\u0026thinsp;MS (2m MH)\u0026thinsp;\u0026gt;\u0026thinsp;SS (2m MH). It is remarkable that the mean WRE was lower in 2023 than in 2024 at both measurement heights and for both crops.\u003c/p\u003e\u003cp\u003eTherefore, the reason could be the generally lower mean WV in 2023 (\u0026Oslash; 2.0 m/s vs. 2024; \u0026Oslash; 2.2 m/s). Furthermore, the WRE in MS is almost the same as in the SS variant, despite the fact that the MS wind sensor was below the maize canopy during part of the growing season (in contrast to the SS sensor which remained to be above the soybean canopy throughout the seasons). This hints at a substantially strong wind flow through the 1.5 m wide maize strip canopy and an overall relatively homogeneous wind field, without considering wind direction effects. For a more detailed analysis, the WRE (during the 2023 and 2024 experimental seasons) is related to wind direction sectors, where we selected 8 wind sectors of 45\u0026deg; each, respectively, within the range of 0-360\u0026deg; (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea-d and Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ea-d). In theory, the wind directions perpendicular to the strip row orientation should cause the highest WRE. This is confirmed in 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea-d), where the highest daily WRE in all variants and measurement heights occurred in the sector 4 (136\u0026ndash;180\u0026deg;), which corresponds to the SE wind direction, which was the dominating wind direction during the measurement period). The ranges from highest to the lowest WRE effects per wind sector of the measurement variants are as follows: Soybean strip (1m MH): 4\u0026thinsp;\u0026gt;\u0026thinsp;5\u0026thinsp;\u0026gt;\u0026thinsp;8\u0026thinsp;\u0026gt;\u0026thinsp;7\u0026thinsp;\u0026gt;\u0026thinsp;3\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026thinsp;\u0026gt;\u0026thinsp;6\u0026thinsp;\u0026gt;\u0026thinsp;2; Soybean strip (2m MH): 4\u0026thinsp;\u0026gt;\u0026thinsp;3\u0026thinsp;\u0026gt;\u0026thinsp;7\u0026thinsp;\u0026gt;\u0026thinsp;6\u0026thinsp;\u0026gt;\u0026thinsp;8\u0026thinsp;\u0026gt;\u0026thinsp;5\u0026thinsp;\u0026gt;\u0026thinsp;2\u0026thinsp;\u0026gt;\u0026thinsp;1, Maize strip (1m MH): 4\u0026thinsp;\u0026gt;\u0026thinsp;7\u0026thinsp;\u0026gt;\u0026thinsp;6\u0026thinsp;\u0026gt;\u0026thinsp;8\u0026thinsp;\u0026gt;\u0026thinsp;3\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026thinsp;\u0026gt;\u0026thinsp;2\u0026thinsp;\u0026gt;\u0026thinsp;5, and Maize strip (2m MH): 4\u0026thinsp;\u0026gt;\u0026thinsp;7\u0026thinsp;\u0026gt;\u0026thinsp;6\u0026thinsp;\u0026gt;\u0026thinsp;8\u0026thinsp;\u0026gt;\u0026thinsp;3\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026thinsp;\u0026gt;\u0026thinsp;2\u0026thinsp;\u0026gt;\u0026thinsp;5.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThere is a good agreement of WRE per sector at (2m MH), (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea-d), where E-SE and W-NW show the highest effects in agreement with the main wind direction of the site. The results at (1m MH), (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ea-d) are more diverse but also show the highest WRE in sector 4 (SE). At (1m MH) the other wind directions show, however, different behavior, which could be explained by channeling effects due to the two different canopy heights of maize and soybean (0.5m vs. 2m). That is, the wind sensor of the soybean strip was placed above the canopy, whereas the wind sensor of the maize strip was placed within the maize canopy, which led to a harmonization of WRE according to wind direction sectors. The lowest WRE, however, was found in all variants in the strip direction sectors 1\u0026ndash;2 (N-NE) and sector 5 (south), supporting the importance of the strip direction in relation to the main wind direction for WRE.\u003c/p\u003e\u003cp\u003eAlthough of an overall similar pattern than in 2023, the highest WRE effect was caused by the WNW wind direction in 2024 (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ea-d). The highest daily WRE in the strip variants and measurement heights occurred in sector 7 (271\u0026ndash;315\u0026deg;), which corresponds to WNW wind directions (perpendicular to the strip row orientation as well). The highest daily WRE in all variants and measurement heights occurred in sector 7 (270\u0026ndash;315\u0026deg;), which corresponds to NW wind direction.\u003c/p\u003e\u003cp\u003eThe ranges from highest to the lowest WRE effects per wind sector of the measurement variants are as follows: Soybean strip (1m MH): 7\u0026thinsp;\u0026gt;\u0026thinsp;4\u0026thinsp;\u0026gt;\u0026thinsp;3\u0026thinsp;\u0026gt;\u0026thinsp;6\u0026thinsp;\u0026gt;\u0026thinsp;5; Soybean strip (2m MH): 7\u0026thinsp;\u0026gt;\u0026thinsp;3\u0026thinsp;\u0026gt;\u0026thinsp;4\u0026thinsp;\u0026gt;\u0026thinsp;5\u0026thinsp;\u0026gt;\u0026thinsp;6, Maize strip (1m MH): 7\u0026thinsp;\u0026gt;\u0026thinsp;3\u0026thinsp;\u0026gt;\u0026thinsp;6\u0026thinsp;\u0026gt;\u0026thinsp;4\u0026thinsp;\u0026gt;\u0026thinsp;5, and Maize strip (2m MH): 7\u0026thinsp;\u0026gt;\u0026thinsp;6\u0026thinsp;\u0026gt;\u0026thinsp;5\u0026thinsp;\u0026gt;\u0026thinsp;4\u0026thinsp;\u0026gt;\u0026thinsp;3. As in 2023, it is shown that the wind sectors from NW and SE (mainly the sectors 3, 4, 6, 7) caused the highest mean WRE, depending on the wind direction frequency during the two seasons. There is a congruence of WRE per sector in the measurement height of (2m MH ) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ea-d), where sector W-NW shows the highest effect in comparison with the main wind direction of the year 2024 (NW). The results at 1m MH; (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ea-d) show the same tendency as the other results, the highest WRE occurs in WS 7 (NW). In 2024, WS classes 1, 2 and 8 did not occur at all during the measurement period.\u003c/p\u003e\u003cp\u003ePrecipitation based water use efficiency\u003c/p\u003e\u003cp\u003eThe development of precipitation based AGDB (above ground dry biomass) WUE followed a similar trend in both years. In 2023, a total of 34.0 t/ha was harvested for maize and soybean combined, with a total WUE of 276.5 kg/m\u0026sup3;. In comparison, the WUE for the sole crops was 147.2 kg/m\u0026sup3; for maize and 56.2 kg/m\u0026sup3; for soybean. In 2024, 25.1 t/ha was achieved for both crops (maize and soybean), with a biomass WUE of 343.3 kg/m\u0026sup3; (Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For the sole crops the harvest was 18.0 t/ha for maize and 6.3 t/ha for soybean and the biomass WUE was 245.9 kg/m\u0026sup3; for maize and 86.7 kg/m\u0026sup3; for soybean. The lower WUE of both crops in 2024 can be attributed to the hotter and drier conditions during the main growing phase (June-July-August), which increased unproductive evaporation losses from the crop stand. Concerning the grain yield WUE a not the same trend can be shown. Comparing the intercropping parcels with the sole crop parcels, in 2023 the maize shows a deficit of (-22 kg/ m\u0026sup3;) and the soybean a surplus of (2.8 kg/ m\u0026sup3;). In 2024, comparing the intercropping parcels with the sole crop parcels the maize showed a deficit of (56.7 kg/ m\u0026sup3;) and the soybean a deficit of (21.7 kg/ m\u0026sup3;), (Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOverview of wind velocity data (1 and 2m measuring height) for 2023, including mean values across replications (n\u0026thinsp;=\u0026thinsp;18x10\u003csup\u003e3\u003c/sup\u003e) and DOY range (171\u0026ndash;283) in combination with the biomass data and yield data and the calculated biomass WUE of above ground dry matter (at harvest) and the calculated yield based WUE\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlot\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMeasurement height*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRange**\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean**\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAGDM / Grain yield***\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBiomass WUE / Grain yield WUE\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u0026ndash;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4\u0026ndash;4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.9/ 6.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e205.3/ 51.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u0026ndash;1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4\u0026ndash;4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.1/ 1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e71.1/ 15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMM\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1m / 2m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ena\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ena\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.6/ 9.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e147.2/ 77.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSM\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1m / 2m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ena\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ena\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.4/ 2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e56.2/ 22.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e*above ground level; n\u0026thinsp;=\u0026thinsp;14.112; **wind velocity m/s; *** tons/hectare as average in all plot replications; \u003csup\u003e1\u003c/sup\u003e bare soil plot; \u003csup\u003e2\u003c/sup\u003e kg/m\u0026sup3;.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOverview of wind velocity data (1 and 2m measuring height) for 2024, including mean values across replications (n\u0026thinsp;=\u0026thinsp;19.3x10\u003csup\u003e3\u003c/sup\u003e) and DOY range (206\u0026ndash;255) in combination with the biomass data and yield data and the calculated biomass WUE of above ground dry matter (at harvest) and the calculated yield based WUE. The REF2 variant represents soybean monocrop plot.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlot\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMeasurement height *\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRange**\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean**\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAGDM / grain yield***\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBiomass WUE / Grain yield WUE\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2\u0026ndash;2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5\u0026ndash;3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.4 / 5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e279.0 / 73.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u0026ndash;2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8\u0026ndash;3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.7 / 0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e64.2 / 12.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSM\u003c/b\u003e\u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3\u0026ndash;7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0-.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSM\u003c/b\u003e\u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7\u0026ndash;4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.3 / 2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e245.9 / 33.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMM\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ena\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ena\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ena\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.0 / 9.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e130.0 / 86.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e*above ground level; n\u0026thinsp;=\u0026thinsp;7056; **wind velocity m/s; *** tons/hectare as average in all plot replications; \u003csup\u003e1\u003c/sup\u003e soybean monocrop plot; \u003csup\u003e2\u003c/sup\u003e kg/m\u0026sup3;.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eTo address the research questions regarding wind breaking effects within a maize\u0026ndash;soybean strip intercrop system, field measurements were conducted over two growing seasons (2023 and 2024). Each season included approximately five months of continuous observations on experimental plots comprising both monocrop and 1.5 m width strip variants, with a row width of 50 cm within the parcels. The strip plots (60 m\u0026sup2; per variant, with two replications) and eight replicates of the overall plots were used for above ground dry biomass (AGDB), yield, and microclimatic measurements, with selected plot variants designated for detailed assessments.\u003c/p\u003e\u003cp\u003eThe first research question focuses on how strip intercropping affects canopy wind velocities and water use efficiency and the second research question focuses on the associated wind reduction effects (WRE) in regard to strip orientation vs. wind direction. Our results indicate that the WRE is strongly influenced by specific 45\u0026deg; wind sectors, which correspond to the main climatic wind directions of the experimental site.\u003c/p\u003e\u003cp\u003eIn 2023 and 2024 maize and soybean growing periods, the main prevailing wind directions were similar but with different contributions, from west to north and from south to southwest, respectively. With the intercropping strips-oriented north\u0026ndash;south, the highest wind reductions in 2023 were observed from the southeast and northwest sectors at both 1 m and 2 m measurement heights. In 2024 the predominant wind direction was higher from west\u0026ndash;northwest, but maximum wind reductions were recorded from the southwest and northwest at 2m above ground level. The wind reduction in both observation years, expressed as a percentage, was between 6.6.-13.3% at the 2m measuring height on both plots relative to the dataset from the INCA grid. At 1m measuring height, the reduction was significantly higher in both crops, ranging between 24 and 44%. This result is comparable to other studies, which reported for example 6.7% wind reduction at 2m measuring height (Sun et al., 1994) for a similar strip intercropping design.\u003c/p\u003e\u003cp\u003eComparable reductions ranging from 17% to 67% have also been reported in studies focusing on the mitigation of wind erosion in connection with the reduction of wind velocities (van Ramshorst et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A detailed analysis of wind velocity revealed significant variability. In 2023, at 2 m measuring height, the highest WV across all plots (INCA grid dataset; MS-maize strip; soybean strip (SS) occurred from south-east (181\u0026ndash;270\u0026deg;). At 1 m measuring height, the dominant WS were 2 (91\u0026ndash;180\u0026deg;) on the REF1 plot, 4 (270\u0026ndash;360\u0026deg;) on the MS plot, and 2 on the SS plot. In contrast, in 2024, at 2 m measuring height, sector 4 (270\u0026ndash;360\u0026deg;) showed the highest WV, while at 1 m measurement height, the dominant WS for both MS and SS was 3 (90\u0026ndash;180\u0026deg;).\u003c/p\u003e\u003cp\u003eDespite interannual differences attributable to varying weather conditions, wind reduction effects were consistently observed. Overall, the mean WRE in both years (2023\u0026ndash;2024) was 0.6- 2.0 m/s at 2 m in the soybean strip and from 0.5 to 2.1 m/s in the maize strip, with elevated but similar ranges in 1m measuring height (1.0-1.2 in the soybean plot (SS) and 1.1\u0026ndash;1.4 in the maize plot (MS)). When analyzed in 45\u0026deg; increments, the highest reductions at 2 m occurred in wind direction sectors 4 and 7 in 2023 and in sectors 7 and 6 in 2024, while at 1 m, sectors 4 and 3 (2023) and sectors 7 and 3 (2024) showed the greatest effects. Notably, the maximum reduction was observed in the soybean plot at 1 m in 2023 (1.9 m/s) and in the maize strip at 2 m in 2024 (4.9 m/s).\u003c/p\u003e\u003cp\u003eWith respect to AGDB WUE, an improvement of 28% (maize) and 21% soybean compared to sole crops was observed in 2023. Concerning the biomass WUE in 2024, the intercropping experiment had a surplus of 11.9% (maize) and a deficit of 26.1% (soybean).\u003c/p\u003e\u003cp\u003eFurthermore, the grain yield WUE showed in 2023 a deficit of 30% (maize) and a surplus of 18.6% (soybean). In 2024 the yield- based WUE was generally negative in comparison to the yield in the sole crops (-43% (maize); -65% (soybean)).\u003c/p\u003e\u003cp\u003eNevertheless, a measurable advantage over sole crops was only soybean (+\u0026thinsp;18.6%) in 2023 present. The results from 2023 are in good agreement with comparable studies, which reported an increased yield- based WUE of 25% and an improvement in biomass WUE of 24% in intercropping systems (Chen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Qian et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In another study (Mao Lilli et al., 2012), biomass WUE ranged from \u0026minus;\u0026thinsp;26.1% to +\u0026thinsp;28%, suggesting that intercropping, due to interactions within the strips, can also negatively affect yield (e.g. in our case study \u0026minus;\u0026thinsp;26 to -65%). This circumstance may explain the 2024 yield- based WUE results, where the advantage of intercropping over sole cropping was not present.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe challenge of increasing irrigation water demand and increasing water scarcity due to ongoing climate change in the study area underscores the importance of water conservation strategies. The design of our experiment focused on analyzing strip intercropping effects for wind protection measures in order to reduce non-productive evapotranspiration to improve WUE. Our findings confirm that the wind reduction effect in the soybean strip is robust across years, despite uncertainties in in-situ measurements. We found an overall positive effect on biomass production and related AGDB WUE of maize and soybean in the strip variants in our specific strip design with relatively small strip widths of 1.5m, however, with one exception for soybean in 2024. Obviously, biomass and grain yield WUE is not only affected by the wind breaking effect, but in combination with other factors, such as shading or other growth limitations, which may dominate under specific circumstances such as in more humid climates. This would need more research, e.g. considering different strip widths effects and row widths.\u003c/p\u003e\u003cp\u003eThe AGDB WUE was variable across the two observed years and in the maize/ soybean monocrop plots (MM, SM) generally lower than in the strip intercropped plots (MS, SS). Only in 2024, the soybean intercropping plots showed lower biomass WUE than the monocrop variants. Concerning the grain yield WUE, the comparison between monocrop and intercropping was not favorable for the intercropping, because it was only in 2023 in the intercropped soybean plot positive (+\u0026thinsp;18.6%). Further research is needed additionally to assess the scalability of these results and integrating intercropping systems of different designs with wind protection. In the context of ongoing climate change new cropping systems will be crucial for sustainable agricultural management, especially in the face of rising temperatures, altered precipitation patterns, and increased yield variability in monocropping systems.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eWUE water use efficiency\u003c/p\u003e\u003cp\u003eAT air temperature\u003c/p\u003e\u003cp\u003eRH relative air humidity\u003c/p\u003e\u003cp\u003eP precipitation\u003c/p\u003e\u003cp\u003eWV wind velocity\u003c/p\u003e\u003cp\u003eWRE wind reduction effect\u003c/p\u003e\u003cp\u003easl above sea level\u003c/p\u003e\u003cp\u003eWMO World Meteorological Organisation\u003c/p\u003e\u003cp\u003eMJJAS May-June-July-August-September\u003c/p\u003e\u003cp\u003eLTM long term mean\u003c/p\u003e\u003cp\u003eMAT mean annual temperature\u003c/p\u003e\u003cp\u003eMH measuring height above ground level\u003c/p\u003e\u003cp\u003eDOY day of year\u003c/p\u003e\u003cp\u003eAGDM above ground dry matter at harvest\u003c/p\u003e\u003cp\u003eREF1 bare soil plot (2023)\u003c/p\u003e\u003cp\u003eREF2 soybean monocrop plot (2024)\u003c/p\u003e\u003cp\u003eMM maize monocrop plot\u003c/p\u003e\u003cp\u003eSM soybean monocrop plot\u003c/p\u003e\u003cp\u003eSS soybean strip intercropping plot\u003c/p\u003e\u003cp\u003eMS maize strip intercropping plot\u003c/p\u003e\u003cp\u003eAGDB above ground dry biomass\u003c/p\u003e\u003cp\u003eARIS agricultural risk information system\u003c/p\u003e\u003cp\u003eINCA Integrated Nowcasting through comprehensive Analysis\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor contributionsJ.E. conceived and designed the study. G.G. and A.S. conducted data gathering and data processing. K.G. helped with statistical analysis in R and other analysis tools, H.W. and J.F. provided the research area, P.W. was part of the advisory board of the doctoral thesis from G.G., and G.G. and J.E. wrote the article.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe field experiment was carried out in the frame of the EU project InterCropValues, funded by the European Commission (EC). In general, I would like to dedicate this article to my father-in-law, Richard Retzl, and J\u0026uuml;rgen Friedel; they sadly passed away during the preparation of this work. Special thanks are extended to Dr. Kerstin Michel for her valuable suggestions and unwavering support in all scientific matters. The authors also acknowledge the BOKU University for covering the submission fee for this open access article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAust, G., Leitgeb, E. (2024) eBOD2 Digitale Bodenkarte \u0026Ouml;sterreichs. Hrsg: Bundesforschungs- und Ausbildungszentrum f\u0026uuml;r Wald, Naturgefahren und Landschaft (BFW). https://bodenkarte.at, (accessed on 7th Februar 2024).\u003c/li\u003e\n\u003cli\u003eBriggs, L. J., \u0026amp; Shantz, H. L. (1913). The water requirement of plants (No. 284-285). US Government Printing Office.\u003c/li\u003e\n\u003cli\u003eCarslaw, D.C. und K. Ropkins, (2012). openair \u0026mdash; an R package for air quality data analysis. Environmental Modelling \u0026amp; Software, Volume 27-28, pp. 52\u0026ndash;61. https://doi.org/10.1016/j.envsoft.2011.09.008\u003c/li\u003e\n\u003cli\u003eChen, H., Anshen, Q.,, Quiang, C., Yantai, G., und Zhadong, L. (2014) Quantification of Soil Water Competition and Compensation Using Soil Water Differences between Strips of Intercropping, 321-330. https://doi.org/10.1007/s40003-014-0134-6\u003c/li\u003e\n\u003cli\u003eChen, G., Kong, X., Gan, Y., Zhang, R., Feng, F., Yu, A., ... \u0026amp; Chai, Q. (2018). Enhancing the systems productivity and water use efficiency through coordinated soil water sharing and compensation in strip-intercropping. Scientific Reports, 8(1), 10494. https://doi.org/10.1038/s41598-018-28612-6\u003c/li\u003e\n\u003cli\u003eComtois, D. (2024). summarytools: Tools to Quickly and Neatly Summarize Data. R package version 1.0.1, https://CRAN.R-project.org/package=summarytools\u003c/li\u003e\n\u003cli\u003eEitzinger, J., Daneu, V., Kubu, G., Thaler, S., Trnka, M., Schaumberger, A., ... \u0026amp; Tran, T. M. A. (2024). Grid based monitoring and forecasting system of cropping conditions and risks by agrometeorological indicators in Austria\u0026ndash;Agricultural Risk Information System ARIS. Climate Services, 34, 100478. https://doi.org/10.1016/j.cliser.2024.100478\u003c/li\u003e\n\u003cli\u003eFeng-yun, Z., Pu-te, W., Xi-ning, Z., \u0026amp; Xue-feng, C. (2012). Water-saving mechanisms of intercropping system in improving cropland water use efficiency. Yingyong Shengtai Xuebao, 23(5). \u003c/li\u003e\n\u003cli\u003eGao, Y., Duan, A., Qiu, X., Li, X., Pauline, U., Sun, J., \u0026amp; Wang, H. (2013). Modeling evapotranspiration in maize/soybean strip intercropping system with the evaporation and radiation interception by neighboring species model. Agricultural Water Management, 128, 110-119. https://doi.org/10.1016/j.agwat.2013.06.020\u003c/li\u003e\n\u003cli\u003eGeoSphere Austria Datenportal. (2024), https://data.hub.geosphere.at/, [accessed on 18th September, 2024 and accessed on 23th October, 2025 (INCA)]\u003c/li\u003e\n\u003cli\u003eHaiden, T., Kann, A., Wittmann, C., Pistotnik, G., Bica, B., \u0026amp; Gruber, C. (2011). The Integrated Nowcasting through Comprehensive Analysis (INCA) system and its validation over the Eastern Alpine region. Weather and Forecasting, 26(2), 166-183. https://doi.org/10.1175/2010WAF2222451.1\u003c/li\u003e\n\u003cli\u003eINSPIRE (2024) INSPIRE Geodatenportal https://inspire-geoportal.ec.europa.eu/srv/ger/catalog.search#/home (accessed on 20th April 2024).\u003c/li\u003e\n\u003cli\u003eIUSS Working Group WRB (2022)https://wrb.isric.org/documents/#:~:text=The%20correct%20citation%20is%3A%20IUSS,creating%20legends%20for%20soil%20maps (accessed on 2th Februar 2024).\u003c/li\u003e\n\u003cli\u003eKirchner,M., Strauss,F., Heumesser,C., und Schmid,E. (2012) Integrative model analysis of adaptation measures to a warmer and drier climate. Jahrbuch der \u0026Ouml;sterreichischen Gesellschaft f\u0026uuml;r Agrar\u0026ouml;konomie (\u0026Ouml;sterreichischen Gesellschaft f\u0026uuml;r Agrar\u0026ouml;konomie) 21(1), 177-186.\u003c/li\u003e\n\u003cli\u003eMao, L., Zhang, L., Li, W., van der Werf, W., Sun, J., Spiertz, H., \u0026amp; Li, L. (2012). Yield advantage and water saving in maize/pea intercrop. Field Crops Research, 138, 11-20. https://doi.org/10.1016/j.fcr.2012.09.019\u003c/li\u003e\n\u003cli\u003eMicrosoft Corporation. (2023). \u003cem\u003eMicrosoft Excel\u003c/em\u003e (Version 16.80) [Software]. available: https://www.microsoft.com/\u003c/li\u003e\n\u003cli\u003eNeudorfer, W., \u0026amp; Weyermayer, H. (2007). Securing groundwater use and reestablishing the water balance by artificial recharge of groundwater in the region of Marchfeld, Austria. Water Practice and Technology, 2(3), wpt2007065. https://doi.org/10.2166/wpt.2007.065\u003c/li\u003e\n\u003cli\u003eNeugebauer, N., \u0026amp; Vuolo, F. (2014). Crop water requirements on regional level using remote sensing data\u0026mdash;A case study in the Marchfeld Region. Photogramm. Fernerkund. Geoinformation, 2014, 369-381. \u003c/li\u003e\n\u003cli\u003eRadke, J. K., \u0026amp; Hagstrom, R. T. (1976). Strip intercropping for wind protection. Multiple Cropping, 27, 201-222.\u003c/li\u003e\n\u003cli\u003eRahman, T., Ye, L., Liu, X., Iqbal, N., Du, J., Gao, R., ... \u0026amp; Yang, W. (2017a). Water use efficiency and water distribution response to different planting patterns in maize\u0026ndash;soybean relay strip intercropping systems. Experimental Agriculture, 53(2), 159-177. https://doi.org/10.1017/S0014479716000260\u003c/li\u003e\n\u003cli\u003eRahman, T., Liu, X., Hussain, S., Ahmed, S., Chen, G., Yang, F \u0026amp; Yang, W. (2017b). Water use efficiency and evapotranspiration in maize-soybean relay strip intercrop systems as affected by planting geometries. PloS one, 12(6), e0178332. https://doi.org/10.1371/journal.pone.0178332\u003c/li\u003e\n\u003cli\u003eRaseduzzaman, M. D. (2016). Intercropping for enhanced yield stability and food security. Department of Biosystems and Technology,. Master Thesis. https://www.researchgate.net/profile/Md-Raseduzzaman/publication/320402915_Intercropping_for_enhanced_yield_stability_and_food_security/links/59e22b10a6fdcc7154d80ce1/Intercropping-for-enhanced-yield-stability-and-food-security.pdf\u003c/li\u003e\n\u003cli\u003eRaza, M. A., Gul, H., Wang, J., Yasin, H. S., Qin, R., Khalid, M. H. B., \u0026amp; Yang, W. (2021a). Land productivity and water use efficiency of maize-soybean strip intercropping systems in semi-arid areas: A case study in Punjab Province, Pakistan. Journal of Cleaner Production, 308, 127-282. https://doi.org/10.1016/j.jclepro.2021.127282\u003c/li\u003e\n\u003cli\u003eRaza, M. A., Yasin, H. S., Gul, H., Qin, R., Mohi Ud Din, A., Khalid, M. H. B., ... \u0026amp; Yang, W. (2022b). Maize/soybean strip intercropping produces higher crop yields and saves water under semi-arid conditions. Frontiers in Plant Science, 13, 1006720. https://doi.org/10.3389/fpls.2022.1006720\u003c/li\u003e\n\u003cli\u003eRevelle, W. (2024). psych: Procedures for Psychological, Psychometric, and Personality Research. Northwestern University, Evanston, Illinois. R package version 2.4.3, https://CRAN.R-project.org/package=psych\u003c/li\u003e\n\u003cli\u003eRStudio Team. (2025). \u003cem\u003eRStudio: Integrated Development Environment for R\u003c/em\u003e (Version 4.3.2) [Software]. Verf\u0026uuml;gbar unter https://www.rstudio.com/\u003c/li\u003e\n\u003cli\u003eSalehi, A., Mehdi, B., Fallah, S., Kaul, H. P., \u0026amp; Neugschwandtner, R. W. (2018a). Productivity and nutrient use efficiency with integrated fertilization of buckwheat\u0026ndash;fenugreek intercrops. Nutrient cycling in agroecosystems, 110, 407-425. https://doi.org/10.1007/s10705-018-9906-x\u003c/li\u003e\n\u003cli\u003eSalehi, A., Fallah, S., \u0026amp; Kaul, H. P. (2017b). Broiler litter and inorganic fertilizer effects on seed yield and productivity of buckwheat and fenugreek in row intercropping. Archives of Agronomy and Soil Science, 63(8), 1121-1136. https://doi.org/10.1080/03650340.2016.1258114\u003c/li\u003e\n\u003cli\u003eStrauss, F., Schmid, E., Moltchanova, E., Formayer, H., \u0026amp; Wang, X. (2012). Modeling climate change and biophysical impacts of crop production in the Austrian Marchfeld Region. Climatic Change, 111, 641-664. https://doi.org/10.1007/s10584-011-0171-0\u003c/li\u003e\n\u003cli\u003eSun, D., \u0026amp; Dickinson, G. R. (1994). A case study of shelterbelt effect on potato (Solanum tuberosum) yield on the Atherton Tablelands in tropical north Australia. Agroforestry Systems, 25, 141-151.\u003c/li\u003e\n\u003cli\u003eThaler, S., Eitzinger, J., Dubrovsk\u0026yacute;, M., \u0026amp; Trnka, M. (2008a). Climate change impacts on selected crops in Marchfeld, Eastern Austria. In 28th conference on agricultural and forest meteorology Vol. 28.\u003c/li\u003e\n\u003cli\u003eThaler, S., Eitzinger, J., Rischbeck, P., Dubrovsky, M., \u0026amp; Trnka, M. (2010b). Vulnerability of crops to climate change in Northeastern Austria. Bulgarian Journal of Meteorology and Hydrology, 15(1), 50-61.\u003c/li\u003e\n\u003cli\u003eThaler, S., Eitzinger, J., Trnka, M., \u0026amp; Dubrovsky, M. (2012c). Impacts of climate change and alternative adaptation options on winter wheat yield and water productivity in a dry climate in Central Europe. The Journal of Agricultural Science, 150(5), 537-555. https://doi.org/10.1017/S0021859612000093\u003c/li\u003e\n\u003cli\u003eThinley, K., Sithup, K., Choden, T., Wangmo, P., Deki, S., Dema, T., ... \u0026amp; Katsura, K. (2024). Establishment of a high-yield intercropping system for maize and legumes under rainfed conditions in eastern Bhutan. Plant Production Science, 1-15. https://doi.org/10.1080/1343943X.2024.2354544\u003c/li\u003e\n\u003cli\u003eQian, C. A. I., Zhan-xiang, S. U. N., Wen-bin, W. A. N. G., Wei, B. A. I., Gui-juan, D. U., Yue, Z. H. A. N. G., ... \u0026amp; Feng-yan, Z. H. A. O. (2022). Yield and water use of maize/soybean intercropping systems in semi-arid western Liaoning. Chinese Journal of Agrometeorology, 43(07), 551. 10.3969/j.issn.1000-6362.2022.07.004\u003c/li\u003e\n\u003cli\u003evan Ramshorst, J. G., Siebicke, L., Baumeister, M., Moyano, F. E., Knohl, A., \u0026amp; Markwitz, C. (2022). Reducing wind erosion through agroforestry: a case study using large eddy simulations. Sustainability, 14(20), 13372. https://doi.org/10.3390/su142013372\u003c/li\u003e\n\u003cli\u003eWallace, J. S. (2000). Increasing agricultural water use efficiency to meet future food production. Agriculture, ecosystems \u0026amp; environment, 82(1-3), 105-119. https://doi.org/j.issn.1000-6362.2022.07.004\u003c/li\u003e\n\u003cli\u003eWilley, R. W. (1990). Resource use in intercropping systems. Agricultural water management, 17(1-3), 215-231. https://doi.org/10.1016/0378-3774(90)90069-B\u003c/li\u003e\n\u003cli\u003eWater, U. N. (2024). Progress on change in water-use efficiency. https://doi.org/10.4060/cd2023en\u003c/li\u003e\n\u003cli\u003eTaiyun Wei and Viliam Simko (2021). R package \u0026apos;corrplot\u0026apos;: Visualization of a Correlation Matrix. (Version 0.92). https://github.com/taiyun/corrplot\u003c/li\u003e\n\u003cli\u003eYin, W., Chai, Q., Zhao, C., Yu, A., Fan, Z., Hu, F., ... \u0026amp; Coulter, J. A. (2020). Water utilization in intercropping: A review. Agricultural Water Management, 241, 106335. https://doi.org/10.1016/j.agwat.2020.106335\u003c/li\u003e\n\u003cli\u003eZhao, Y., Fan, Z., Hu, F., Yin, W., Zhao, C., Yu, A., \u0026amp; Chai, Q. (2019). Source-to-sink translocation of carbon and nitrogen is regulated by fertilization and plant population in maize-pea intercropping. Frontiers in Plant Science, 10, 891. https://doi.org/10.3389/fpls.2019.00891\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"agrometeorology, adaption measure, microclimatic conditions, strip intercropping, wind reduction effect","lastPublishedDoi":"10.21203/rs.3.rs-8059550/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8059550/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntercropping has been proposed as an alternative to conventional monoculture systems by improving biomass water use efficiency (WUE) and grain yield based WUE through modified microclimatic conditions. However, little is known about its potential to mitigate wind velocities (WV) in semi-humid Central European environments. A two-year field experimental case study (2023\u0026ndash;2024) was conducted to assess wind-breaking effects in a maize\u0026ndash;soybean strip intercropping system. Treatments included sole crop and strip-intercropping plots, where microclimatic and crop data were collected. WV and wind direction (WD) of the treatments were measured at 1m and 2m above ground. Reference 2m above ground WV from the Integrated Nowcasting through Comprehensive Analysis (INCA) in both years were used for comparison between the two years. Wind reduction effects (WRE) were further analyzed in relation to wind direction sectors (WS). Strip intercropped plots showed significantly reduced wind velocities in respect to the 2m INCA reference, with the strongest effects in soybean (SS) and maize strips (MS) at 1m measurement height. In 2023, mean reductions ranged from 0.6\u0026ndash;1.0 m/s (SS) and 0.5\u0026ndash;1.4 m/s (MS), while in 2024, reductions ranged from 0.7\u0026ndash;1.2 m/s (SS) and 0.7\u0026ndash;1.1 m/s (MS). The highest WRE occurred in the climatological main wind directions (270\u0026ndash;360\u0026deg; in 2023; 315\u0026ndash;360\u0026deg; in 2024), consistent with the north\u0026ndash;south strip and row orientation. Both, above ground dry biomass and grain yield based WUE calculations largely confirmed positive effects of wind reduction with some differences between 20203 and 2024 due to weather conditions. Wind reduction supports not improved grain yield based WUE (2023: -7.7 kg/m\u0026sup3; (SS), -21.8 kg/m\u0026sup3; (MS); 2024: -21.7 kg/m\u0026sup3; (SS), -13.4 kg/m\u0026sup3; (MS)) in comparison to mono cropped plots. The biomass WUE compared to the mono cropped plots was in 2023 positive (+\u0026thinsp;14.9 kg/m\u0026sup3; (SS) and 58.1 kg/m\u0026sup3; (MS), while in 2024 the biomass WUE was negative (-181.7 kg/m\u0026sup2; (SS) and \u0026minus;\u0026thinsp;192.3 kg/m\u0026sup3; (MS)). The outcomes are influenced by strip width and crop-specific interactions (i.e. biomass WUE), highlighting the importance of strip design such as strip orientation in respect to main wind directions for optimizing microclimatic benefits.\u003c/p\u003e","manuscriptTitle":"Effects of a maize- soybean strip intercropping system on canopy level wind velocities in a semi-humid region of Central Europe","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-20 16:04:16","doi":"10.21203/rs.3.rs-8059550/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-19T02:18:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-16T15:55:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"227114182998681997119079371438974528108","date":"2026-02-24T13:44:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232846026414368531529416646907572026081","date":"2026-01-29T02:51:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249421609404391522854904988173244638678","date":"2025-12-16T17:03:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111187205171973537193207767029555924498","date":"2025-12-01T07:09:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-11T06:35:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-10T04:40:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-10T04:39:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Climatology","date":"2025-11-07T18:21:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9027af6a-bae2-4d44-b906-b10c6c5f8bf4","owner":[],"postedDate":"November 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T08:55:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-20 16:04:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8059550","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8059550","identity":"rs-8059550","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}