Sheltered by trees – long-term yield dynamics in temperate alley cropping agroforestry with changing water availability

doi:10.21203/rs.3.rs-4861911/v1

Download PDF

Research Article

Sheltered by trees – long-term yield dynamics in temperate alley cropping agroforestry with changing water availability

https://doi.org/10.21203/rs.3.rs-4861911/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

As warm season droughts increase in frequency due to climate change, causing severe yield losses especially among cereal crops, European agriculture is in dire need of adaptation. While agroforestry is widely regarded as a key adaptation measure, little is known on how yield performance is influenced by changing water availability. Therefore, we assessed the yield dynamics of five winter crops (winter wheat, triticale, winter barley, winter pea, and rapeseed) during seven growing seasons (2012 to 2023) in a well-established (since 2008) alley cropping agroforestry trial site in Southwestern Germany. The trial integrates three different agroforestry practices in a randomized block design: i) willow short-rotation coppice, ii) walnut trees for nut production, and iii) diverse hedgerows. The relationship between crop yield and climatic water balance was analysed using a linear mixed-model. In this unique long-term comparison, we demonstrate that individual alley cropping practices exhibited distinct yield patterns with increased distance to tree rows. In contrast to the willow short rotation coppice, walnut and hedgerows did not evoke significant winter crop yield declines in their close proximity. While in the walnut plots yields did not significantly vary at all with distance to tree rows, yields adjacent to hedge rows declined significantly towards the alley center. Moreover, inter-annual variation in water availability significantly influenced yield distribution across these distances. While yield response to changing climatic water balance varied with aspect, the tree rows overall contributed to stabilizing crop yields under fluctuating water availability as yields did not vary significantly close to trees. Our results underline the potential of agroforestry to sustain yields in the face of increasingly variable water availability. Therefore, substantiating the contribution of alley cropping agroforestry to resilient farming systems facing increasingly variable weather conditions, informing planning, policy support and agroforestry practice at advancing climate resilient agroforestry in temperate regions.

short rotation coppice

walnut

hedge

climate change adaptation

crop yield

climatic water balance

drought

tree-crop interaction

agroecological intensification

alley cropping

In the face of climate change, European agriculture is in dire need of adaptation options. Warm season droughts are becoming more frequent in Central Europe (Markonis et al., 2021), already causing severe yield losses (Brás et al., 2021; Lüttger & Feike, 2018). This forces the necessity to bolster more resilient and diversified cropping systems and agroforestry is widely regarded as a key adaptation measure in the face of increasingly extreme weather events (IPCC, 2019; van Noordwijk et al., 2021). The regulating effects of woody components on the microclimate have been observed by various studies, namely the reduction of temperature extremes, wind speed, and evaporation (Jacobs et al., 2022; Kanzler et al., 2019; Schoeneberger et al., 2012; Swieter et al., 2022). This microclimate amelioration benefits a more climate-resilient agricultural production, among other agroforestry-inherent traits such as biodiversity enhancement, improved soil fertility, and carbon sequestration (Cardinael et al., 2021).

However, competition for light, water, and soil nutrients between arable crops and woody components has been reported to negatively affect crop yields in agroforestry systems (Arenas-Corraliza et al., 2022). In temperate agroforestry systems light is considered to be the most limiting factor for agricultural production (Ehret et al., 2015). The reduction in photosynthetically active radiation caused by the tree stands has been shown to result in yield and quality decline (sometimes up to 50% yield decrease) in field crops such as various cereals and legumes (Artru et al., 2017; Dufour et al., 2013; Reynolds et al., 2007). Most cultivars of arable crops in temperate climates have been selected for full light conditions. Therefore, it is important to understand how they respond under altered light conditions (Arenas-Corraliza et al., 2022), and to what degree other factors such as seasonal climatic changes and aspect arrangements can influence the net effects of shading (Anderson et al., 2009; Cardinael et al., 2021; Swieter et al., 2022).

While in a tropical, sub-tropical and Mediterranean context the benefit of agroforestry for agricultural yields has been established for several production systems (Arenas-Corraliza et al., 2022; Gomes et al., 2020) and is projected to be of increasing importance in the face of climate change (Cardinael et al., 2021), there is still a large research gap for temperate climates. Alley cropping - the intentional integration of trees and crops with widely spaced tree rows and in-between crop alleys (Wolz & DeLucia, 2018) - is an especially attractive form of agroforestry for temperate climates, as it offers a low-input system (via improved water management and nutrient cycling) for the simultaneous growth of food production and biomass for improved land use (Quinkenstein et al., 2009). Additionally, the windbreak and shade provided by the tree rows can reduce water loss and improve soil moisture retention due to the lower radiation intensity, reduced wind speed, decreased surface run-off and more moderate soil temperatures (Bird, 1998; Jacobs et al., 2022; Quinkenstein et al., 2009). The effects on air temperature, relative humidity, and evapotranspiration show more varied results, however (Jacobs et al., 2022). Variation among results (yield, water use, microclimate effects, soil effects, etc.) in agroforestry studies is often attributed to differences in tree stand and management (Jacobs et al., 2022; Luedeling et al., 2016). However, our knowledge base on the effects of alley cropping is strongly shaped by research on the management of trees for biomass production, leaving major knowledge gaps when trees are managed for other uses, such as fruit and nut production (Wolz et al., 2018). Many studies in the field identify topography, soil, climate, tree and crop cultivar, canopy management, etc. as the prominent factors influencing microclimate, yield, and water balance (Jacobs et al., 2022; Majaura et al., 2024). Water competition has been shown to vary depending on the tree selection, crops, canopy structure, and of course rainfall patterns (Wang et al., 2021). Furthermore, the spatial distance between the tree rows may play a key role in crop yield and performance due to its influence on competition factors (Ivezić et al., 2021; Quinkenstein et al., 2009). Due to these variable factors, the overall influence that air temperature and relative humidity, together with factors of tree canopy, have on end water use and crop yields are still uncertain and require more detailed investigation (Bird, 1998; Grace, 1988; Jacobs et al., 2022; Swieter et al., 2022). Furthermore, despite a growing body of research on the various effects of agroforestry, there remains a significant knowledge gap regarding the variability in yield performance, especially relative to water availability. The heterogeneous effect of tree rows on crop yields in relation to water availability has been acknowledged for decades (Kort, 1988; Pelton, 1967). However, studies analysing yield data within alley cropping systems over several years are rare, limiting our understanding of yield dynamics in respect to inter-annual weather variability (Swieter et al., 2022). Long-term data is necessary to make more robust estimates of the dynamic performances of yields in temperate agroforestry, as here the comparison of dry and wet years is possible, allowing to draw more conclusive relationships between agroforestry practices and yield dynamics across varying weather conditions, namely water surplus and deficit.

This study addresses the uncertainties in the relationship between agroforestry practices and interannual fluctuations in water availability. We do this by examining yield data on a well-established silvoarable agroforestry trial site (established in 2008) at the experimental station Ihinger Hof of the University of Hohenheim in Stuttgart, Germany, one of the oldest, still running agroforestry experiments in Germany (Fig. 1). The yield data was analysed for 7 cropping seasons within a 12-year period, from five different winter crops (winter wheat, triticale, winter barley, winter pea, and rapeseed) grown in three alley cropping systems (short-rotation coppice, walnut trees for nut production and diverse autochthonous hedges). This allows for a comprehensive investigation, wherein we assume to see yield pattern differences dependent on the differing tree components within the system and improved yield performance in years with low climatic water balance (CWB). The findings are expected to inform adaptation strategies to climate change through agroforestry practices. Incorporating the CWB into our analysis will elucidate the relationship between agroforestry and water availability, highlighting the potential benefits or drawbacks of these practices in a changing climate.

Figure 1 Impression of the agroforestry alley cropping trial with walnut trees, short rotation coppice and diverse hedges established at the experimental station Ihinger Hof, southwest Germany, in 2008. The photo was taken at 10 am on the 22nd of September 2022 (Source: Veronika Geisler)

2.1 Study site / Experimental design

The study site covering 7.1 ha is located at the Agricultural Experimental Station Ihinger Hof of the University of Hohenheim (48.7 °N, 8.9 °E, 475 m asl) in southwestern Germany (Figure 2). The mean annual temperature measured locally 2012 – 2023 is 9.8 °C and mean annual precipitation amounts to 661 mm. Between May and August, winds are mainly blowing from NW – SW. The soil is a loess-derived Luvisol with soil textures ranging from loam to clay, with loamy clay being most prevalent. The alley-cropping agroforestry trial was established in autumn 2007 and features three different tree row configurations representing the diverse characteristics of temperate alley-cropping practice (Wolz & DeLucia, 2018):

Willow short rotation coppice (SRC): willow hybrid clone “Tordis” (Salix schwerinii × viminalis), planted in three double rows spaced 2 m between and 0.75 m among each other, spacing along the row is 0.6 m (Figure 2 a)). Distance of tree rows to the crop alley is ~0.9 m Willows are harvested every three years.
Walnuts: productive nut-bearing Juglans regia variety “Weinheimer (No. 139)”, planted in double rows with rows spaced 4 m apart and trees at 7.5 m distance to each other. Distance of tree rows to the crop alley is 2 m (Figure 2 b).
Autochthonous hedge: features a diverse mix of woody species, typical for their occurrence in the agricultural landscape. The hedge was planted in three parallel rows with a spacing of 1.5 m between the rows and the plants within each row. Distance of hedge rows to the crop alley is 2.5 m (Figure 2 c).

The three different alley-cropping agroforestry practices are arranged in a block design with four replicates (Figure 2 e). Tree rows are oriented 23.03° clockwise from N (SSW – NNE), 8 m wide and spaced 48 m apart. The area between tree rows, where crops are grown, was divided into eight sections each of 6 m width, thus, reflecting a transect of proximity to tree rows: One plot comprises four sections (0-6, 6-12, 12-18, and 18-24 m distance) east and west of the tree rows of one alley cropping practice as indicated by colouring in Figure 2 e. Three plots associated to the same tree row, comprising the three alley cropping practices, form one block. In total, the trial features four blocks and 12 plots.

Figure 2 The alley cropping experimental site Ihinger Hof. Tree row composition and spacing within the three alley cropping practices is shown in panel a)-c), Crosses are exemplary demarcations highlighting tree spacing. Arable alleys are shown in panel d) as well as their configuration in a block design (e)). Plot boundaries are deliniated in bold gray, all plots associated to one tree row collectively form a block. Panel d) was designed using icons made by Freepik, Georgy and Aranagraphics from www.flaticon.com.

Figure 3 Distribution of climatic water balance and annual crops over time.

2.2 Data

We used yield data from five different arable winter crops: rapeseed (Brassica napus L.), triticale (× Triticosecale Wittmack), winter barley (Hordeum vulgare), winter pea (Pisum sativum L.) and winter wheat (Triticum aestivum L.), grown in seven years between 2012 and 2023 (see Figure 3). Yield data was collected over the entire trial site by a GPS-tracked New Holland harvester model CR960 continuously measuring mass flow, providing data with a resolution of 6 m perpendicular to the tree row x 1 m along the alley (Figure 2 d). Only data points from between tree rows and to the west of the first tree row were considered for analysis to avoid confounding yield depression effects of the headland. In total 28174 data points were used for the analysis. Yield data was associated with the closest tree row block (Figure 2 e).

To analyse the relationship between yields in agroforestry and annual weather conditions, we used data from the weather station at the Ihinger Hof, located 1 km to the east of the site. Incorporating water – energy interactions, water-balance-based variables are proposed as meaningful estimates of the hydrologic and energetic environment experienced by plants (Fisher et al., 2011; Stephenson, 1990). For this purpose, climatic water balance (CWB) was calculated as the difference between precipitation and potential evapotranspiration from late spring to summer (between May and August) (Figure 3). The time period was selected as relevant for the assessment of tree row effects on crop yields with respect to the timing of tree leaf development in spring and sensitivity of crops to drought in summer (Gobin, 2012).

We used the number of growing seasons undergone by the trees as a regressor variable to analyse yield dynamics in relation to the age of the agroforestry system. In the case of the willow SRC, growing seasons were counted with the last harvest of woody biomass as a respective starting point (2012, 2016, 2019 and 2022).

2.3 Statistical analysis

We used a mixed modelling approach to analyse spatial yield dynamics across winter crops within the three different alley cropping practices and their interaction with CWB. Multi-level modelling is proposed as a feasible approach to address spatial and temporal complexity which challenges statistical analysis of agroforestry trials (Golicz et al., 2023). Crop yield was used as the response variable and collectively analysed across all years studied. Blocks (BLOCK) were modelled as a fixed effect and plots (PLOT) as random effects. The different agroforestry practices (TREAT) were integrated as a fixed effect. Moreover, the year (YEAR) effect and its interactions with tree row, alley cropping practice and distance were modelled as random effects. To model the influence of changing CWB on yields in relation to alley cropping practice, distance to tree rows and aspect, CWB was added to the model as a fixed effect with linear and quadratic terms as well as their interaction with alley cropping practice, aspect (ASPECT), i.e. crops grown east or west from the tree row, and distance classes (DIST) 0-6, 6-12, 12-18 and 18-24 m from the tree row . Prior to modelling, CWB was divided by 100 to better match its scale to the remaining variables. Additionally, both the crop species (CROP) and age (AGE) were used as fixed covariates in order to distinguish their effect from that of CWB.

The final model is structured as follows:

Response: YIELD ~

Fixed: BLOCK + CROP + TREAT + DIST + ASPECT + TREAT:DIST + TREAT:ASPECT +

TREAT:ASPECT:DIST +

AGE + CWB + I(CWB^2) + TREAT:CWB + TREAT:I(CWB^2) + DIST:CWB + DIST:I(CWB^2) + ASPECT:CWB + ASPECT:I(CWB^2) + TREAT:DIST:CWB + ASPECT:DIST:CWB + TREAT:ASPECT:CWB + TREAT:ASPECT:DIST:CWB

Models were built with the glmmTMB-R-package (Brooks et al., 2024).

The Type II Wald chi-squared test was used for analysis of variance. Respecting marginality (Piepho & Edmondson, 2018) in the analysis of variance, the statistics of linear terms were not considered where corresponding quadratic terms were present. Subsequently, the emmeans-R-Package (Lenth, 2024) was used to conduct Tukey-Tests for pairwise post hoc comparisons. Estimated marginal means were calculated for categorical predictor variables and estimated marginal means of linear trends for reporting estimates of the effect of CWB on yields (Lenth, 2024; Searle et al., 1980). Moreover, we quantified the explained variance for LMMs via the “r.squaredGLMM” function from the MuMIn-R-package (Bartoń, 2023), yielding a marginal R² (mR²) and a conditional R² (cR²) based on (Nakagawa et al., 2017). While the latter quantifies the variance explained by the fixed effect and the random effect combined, the marginal R² solely accounts for the variance explained by the fixed effect.

3.1 Effects of alley cropping agroforestry on winter crop yields

Our model on yield dynamics in three different alley cropping agroforestry systems revealed significant differences in yield dynamics among the studied winter crops. With a marginal R² of 0.68 the fixed effects explained substantial variation in the analysed yield data. Adding the random effects resulted in a conditional R² of 0.77. We showed a significant variation in yields between arable crops (CROP) and distance to tree rows (DIST) (Table 1). No significant variation occured between blocks and agroforestry practices, with aspect and age of the system. Aspect did not have a significant effect on yield levels, neither as a single predictor, nor in interaction with different alley cropping practices and distance to tree rows (TREAT:ASPECT, DIST:ASPECT).

Table 1 Effects of agroforestry variants, distance to trees, aspect and climatic water balance on winter crop yields in alley cropping agroforestry.* significant at alpha = 0.05

Yield dynamics within alley cropping agroforestry systems are shown to be significantly influenced by the proximity of crops to tree rows (Jacobs et al., 2022). Correspondingly, our analysis revealed distance to tree rows to significantly affect winter crop yields in the alley cropping agroforestry trial. However, when comparing the alley cropping practices (willow SRC, walnut, and autochthonous hedge), different patterns emerged (TREAT:DIST, Table 1).

In the willow short rotation coppice plots yield levels were significantly lower closest to the tree rows (0–6 m) compared to crops at an increasing distance (Fig. 4). This is in line with other studies on yields in alley cropping agroforestry, particularly with SRC (Kanzler et al., 2019; Swieter et al., 2019, 2022). Lower yields close to the trees can be attributed to a shift from competition (for light, nutrients, and water) dominated by tree-crop interactions to a sheltering effect that benefits the amelioration of microclimatic conditions further away from the tree rows (Jacobs et al., 2022; Majaura et al., 2024; Swieter et al., 2022). However, the pattern of increasing yields with distance to tree rows could not be observed for the other two alley cropping practices (Fig. 4).

Figure 4 Winter crop yields with distance to tree rows for three different alley cropping practices. Yields shown are averaged over the five winter crops studied. * significant at alpha = 0.05

In contrast to the willow SRC system, yield levels directly adjacent to the hedges and walnut trees were not significantly different compared to the remaining distance classes. While in the walnut system no significant effect of distance to tree rows could be observed, yield levels in the hedge system were lowest at 18–24 m away from the tree rows. The lack of significant yield effect throughout the cropping alley in relation to the walnut tree rows could be attributed to the relatively wide spacing of trees. In addition, late budding of the species in conjunction with a high crown could further have contributed to a reduced competition for light in comparison to the other systems (Bohn Reckziegel et al., 2022). Like the willow SRC rows, the hedges feature a dense structure, making it surprising that no significant yield declines occurred in the first meters from the tree rows as it was observed for the willow SRC. This might be attributed to an ameliorated sheltering effect provided by the hedge row system which significantly improves the protective microclimatic conditions; as a result, yields on the leeward side of windbreaks are known to benefit most from their sheltering (Baker et al., 2018; Kort, 1988; Pelton, 1967). Next to sheltering other tree crop-interactions could facilitate the contrasting yield distribution between hedge and SRC. In this context different leaf litter inputs and competition for nutrients could be relevant (Pardon et al., 2020) since in willow SRC rows nutrients are recurrently exported at harvest. Depending on the conditions in situ, and varieties involved, complex tree – crop interactions result in variable yield responses (Artru et al., 2017; Kanzler et al., 2019; Pardon et al., 2020; Swieter et al., 2022). Existing literature abundantly illustrates that different species, pruning techniques, and management practices significantly affect yield and performance within agroforestry systems, with each system's unique dynamics leading to varying results (Jacobs et al., 2022). As research on alley cropping agroforestry is strongly focusing on biomass producing systems, however, significant knowledge gaps remain on the effects of alley cropping agroforestry designed for other uses such as fruit and nut production (Wolz et al., 2018). Therefore, the different yield patterns observed in direct comparison between three different alley cropping practices highlight the diverse impact that agroforestry practices can create on a field level. It is important to properly differentiate between these practices when communicating their effects. This underscores the importance to further investigate the complex tree – crop interactions that shape these effects in different system configurations.

Moreover, it needs to be stressed that yield patterns are not stable and vary with changing onsite conditions (Baker et al., 2018; Rivest & Vézina, 2015), as explored in the next section. Thus, this mean yield pattern with distance to trees might not reflect yearly response curves.

3.2 Effects of water availability on yield in temperate alley cropping agroforestry

Due to the increased concern of declining yields resulting from more frequent droughts and higher temporal variability of precipitation (Brás et al., 2021), the compounding effect of water availability on yield across years was especially pertinent in the investigated agroforestry systems. Our data shows that CWB influenced yield dynamics in interaction with distance and aspect to trees (DIST:ASPECT:CWB, Table 1), but no significant variation with yield data as a single predictor. The observed influence of CWB on yields did not significantly vary between tree components: thus, in this section the alley cropping practices are looked at in composite.

We see a significant contrast in yield responses to CWB with distance to tree rows between the west- (windward) and east-facing (leeward) sides. The analysis of estimated marginal means of linear trends at respective distance classes for CWB further substantiates that. On the leeward side (ESE), yields 12–24 m away from the tree rows were significantly influenced by CWB levels (Fig. 5, Table 2) with yields benefited from higher CWB and decreased with declining CWB. In contrast, crop yields closer to trees and up to 12 m away remained more stable, not significantly changing with CWB. In dry years this resulted in more evenly distributed yields with distance to tree rows. In an alley cropping system the tree rows create a gradient of microclimatic effects (Jacobs et al., 2022). In a system of several tree rows, the wind speeds are known to be lowest on the leeward side, increasing with distance from the tree strip. This protective effect of the tree row creates a shelter that reduces evaporative loss on the soil and improves temperature-soil-water relationships (Aase & Siddoway, 1976). Additionally, the lower air temperature in shaded zones allows for reduced transpiration in the crops, which would be especially relevant on hot and sunny days (Brandle et al., 2004; Kanzler et al., 2019). These effects have the potential, then, to materialize as boosted growth performance due to improved water availability over the growing season (Rosenberg et al., 1983), which is in alignment with what we see here, where significant drought-induced yield depressions do not occur close to the tree rows (Fig. 5).

The opposite pattern could be observed on the windward (WNW) side, where yield levels varied significantly in closest proximity to tree rows. This could be attributed to the downwind distance from tree rows, in interaction with their orientation. A tree row orientation tilted perfectly perpendicular to the main wind direction would further optimize the sheltering at the most exposed western side of tree rows. Therefore, sheltering against wind should receive special attention in the design of agroforestry systems adopted to address climate change. Moreover, this windward side is also most subjected to shading, reducing exposure to the morning sun. This could be more detrimental under dry conditions when photosynthetic activity is least limited by stomatal conductance in the morning (Hirasawa & Hsiao, 1999; Kruger & Sackett, 2024).

Figure 5 Yield distribution in alley cropping agroforestry with changing climatic water balance on both sides of tree rows based on estimated marginal means at maximum (87 mm, blue) and minimum (-334 mm, red) climatic water balance observed in this study. Yields were averaged over the five winter crops studied. WNW = west northwest, ESE = east southeast. * significant at alpha = 0.05

Considering the combined influence of tree rows on yields from both the windward and leeward sides under changing water conditions, there was an evident mediating effect wherein yields are not significantly influenced by changing CWB up to 12 m distance (Table 2). With increasing distance, yield levels were fluctuating significantly with changing CWB at 12–18 m and 18–24 m away from the tree row, where yields increase by 0.72 (p = 0.046) and 0.71 t/ha (p = 0.049) respectively by every additional 100 mm CWB. The opposite is true in case of dry conditions. Thus, in total, tree rows buffer the inter-annual effect of heterogenous water availability.

Table 2 Model statistics in relation to a change in CWB obtained via the EMTRENDS function showing the effect on yields at different distance classes and aspect. CWB-trend shows the estimated yield change in t/ha with every 100 mm increase in CWB. WNW = west northwest, ESE = east southeast, df = 28100 * significant at alpha = 0.05

Agroforestry is widely advocated as an adaptation strategy for enhancing agricultural resilience to extreme climates. Our findings contribute to understanding long-term yield dynamics within agroforestry practices and under variable water availability. Through this novel direct comparison between alley cropping practices, we were able to show both the variation of yield dynamics between different alley cropping practices and that tree rows buffer the effects of variable weather conditions on winter crop yields. We showed that while not differing in overall yield levels, spatial distribution of yields in alley cropping with walnut trees and diverse hedges features patterns significantly deviated from the typical transition of low yields close to tree rows to higher yields towards the alley centre. In walnut plots yields were not changing significantly with distance to trees and adjacent to hedges yields dropped towards the alley centre. While spatial yield patterns vary, the buffering of yield variability in relation to changing water availability is consistent between agroforestry practices. This effect is predominantly carried by the leeward side of tree rows. The heterogenous microclimate of alley cropping agroforestry holds the potential to provide stabilized yields for mitigation of adverse climate change effects. Understanding yield patterns in different alley cropping systems and their response to changes in CWB can inform targeted management practices such as choice of row distances and cultivar selection to optimize overall performance in the long-term. Our long-term data demonstrates that continued research and monitoring are essential for optimizing these systems to maximize yields and develop robust strategies against the backdrop of climate change-induced weather extremes.

Acknowledgements

We gratefully acknowledge the late Professor Wilhelm Claupein, whose foresight in 2008 to establish the agroforestry field trial at the experimental station Ihinger Hof made this research possible. Special thanks also to all the technicians and supervisors involved from the experimental station of the Ihinger Hof.

Funding

This work was supported by the Eva Mayr-Stihl Foundation through funding of the project “Coordination Office for Agroforestry Systems Research (kAFo)” and funds of the Federal Ministry of Food and Agriculture (BMEL) 636 based on a decision of the Parliament of the Federal Republic of Germany via the Federal Office for Agriculture and Food (BLE) 637, grant number 2822HUM010 (project "Humax").

Conflicts of interest

The authors declare no conflict of interest.

Ethics approval

Not applicable

Consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data, material and code

The datasets and code generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Author Contribution

A.H.S. and O.K. conceptualized and designed the study. Data preparation, analysis and visualization were performed by O.K. with contribution of A.H.S.. H.P.P. helped with the choice of model for statistical analysis. The first draft of the manuscript was written by J.M. and O.K. and all authors were involved in the review and revision of this manuscript. A.H.S. supervised the study. All authors read and approved the final manuscript.

Aase, J. K., & Siddoway, F. H. (1976). Influence of Tall Wheatgrass Wind Barriers on Soil Drying. Agronomy Journal, 68(4), 627–631. https://doi.org/10.2134/agronj1976.00021962006800040024x
Anderson, S. H., Udawatta, R. P., Seobi, T., & Garrett, H. E. (2009). Soil water content and infiltration in agroforestry buffer strips. Agroforestry Systems, 75(1), 5–16. https://doi.org/10.1007/s10457-008-9128-3
Arenas-Corraliza, M. G., López-Díaz, M. L., Rolo, V., Cáceres, Y., & Moreno, G. (2022). Phenological, morphological and physiological drivers of cereal grain yield in Mediterranean agroforestry systems. Agriculture, Ecosystems & Environment, 340, 108158. https://doi.org/10.1016/j.agee.2022.108158
Artru, S., Garré, S., Dupraz, C., Hiel, M.-P., Blitz-Frayret, C., & Lassois, L. (2017). Impact of spatio-temporal shade dynamics on wheat growth and yield, perspectives for temperate agroforestry. European Journal of Agronomy, 82, 60–70. https://doi.org/10.1016/j.eja.2016.10.004
Baker, T. P., Moroni, M. T., Mendham, D. S., Smith, R., & Hunt, M. A. (2018). Impacts of windbreak shelter on crop and livestock production. Crop and Pasture Science, 69(8), 785–796.
Bartoń, K. (2023). MuMIn: Multi-model inference (1.47. 5).
Bird, P. R. (1998). Tree windbreaks and shelter benefits to pasture in temperate grazing systems. Agroforestry Systems, 41(1), 35–54. https://doi.org/10.1023/A:1006092104201
Bohn Reckziegel, R., Sheppard, J. P., Kahle, H.-P., Larysch, E., Spiecker, H., Seifert, T., & Morhart, C. (2022). Virtual pruning of 3D trees as a tool for managing shading effects in agroforestry systems. Agroforestry Systems, 96(1), 89–104. https://doi.org/10.1007/s10457-021-00697-5
Brandle, J. R., Hodges, L., & Zhou, X. H. (2004). Windbreaks in North American agricultural systems. In P. K. R. Nair, M. R. Rao, & L. E. Buck (Eds.), New Vistas in Agroforestry: A Compendium for 1st World Congress of Agroforestry, 2004 (pp. 65–78). Springer Netherlands. https://doi.org/10.1007/978-94-017-2424-1_5
Brás, T. A., Seixas, J., Carvalhais, N., & Jägermeyr, J. (2021). Severity of drought and heatwave crop losses tripled over the last five decades in Europe. Environmental Research Letters, 16(6), 065012. https://doi.org/10.1088/1748-9326/abf004
Brooks, M., Bolker, B., Kristensen, K., Maechler, M., Magnusson, A., & McGillycuddy, M. (2024). glmmTMB: Generalized Linear Mixed Models using Template Model Builder. R Packag Vers, 1(1), 9.
Cardinael, R., Cadisch, G., Gosme, M., Oelbermann, M., & van Noordwijk, M. (2021). Climate change mitigation and adaptation in agriculture: Why agroforestry should be part of the solution. Agriculture, Ecosystems & Environment, 319, 107555. https://doi.org/10.1016/j.agee.2021.107555
Dufour, L., Metay, A., Talbot, G., & Dupraz, C. (2013). Assessing Light Competition for Cereal Production in Temperate Agroforestry Systems using Experimentation and Crop Modelling. Journal of Agronomy and Crop Science, 199(3), 217–227. https://doi.org/10.1111/jac.12008
Ehret, M., Graß, R., & Wachendorf, M. (2015). The effect of shade and shade material on white clover/perennial ryegrass mixtures for temperate agroforestry systems. Agroforestry Systems, 89(3), 557–570. https://doi.org/10.1007/s10457-015-9791-0
Fisher, J. B., Whittaker, R. J., & Malhi, Y. (2011). ET come home: Potential evapotranspiration in geographical ecology. Global Ecology and Biogeography, 20(1), 1–18.
Gobin, A. (2012). Impact of heat and drought stress on arable crop production in Belgium. Nat. Hazards Earth Syst. Sci., 12(6), 1911–1922. https://doi.org/10.5194/nhess-12-1911-2012
Golicz, K., Piepho, H.-P., Minarsch, E.-M. L., Niether, W., Große-Stoltenberg, A., Oldeland, J., Breuer, L., Gattinger, A., & Jacobs, S. (2023). Highlighting the potential of multilevel statistical models for analysis of individual agroforestry systems. Agroforestry Systems, 97(8), 1481–1489. https://doi.org/10.1007/s10457-023-00871-x
Gomes, L. C., Bianchi, F. J. J. A., Cardoso, I. M., Fernandes, R. B. A., Filho, E. I. F., & Schulte, R. P. O. (2020). Agroforestry systems can mitigate the impacts of climate change on coffee production: A spatially explicit assessment in Brazil. Agriculture, Ecosystems & Environment, 294, 106858. https://doi.org/10.1016/j.agee.2020.106858
Grace, J. (1988). 3. Plant response to wind. Proceedings of an International Symposium on Windbreak Technology, 22–23, 71–88. https://doi.org/10.1016/0167-8809(88)90008-4
Hirasawa, T., & Hsiao, T. C. (1999). Some characteristics of reduced leaf photosynthesis at midday in maize growing in the field. Field Crops Research, 62(1), 53–62. https://doi.org/10.1016/S0378-4290(99)00005-2
IPCC. (2019). Climate Change and Land: An IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems.
Ivezić, V., Yu, Y., & Werf, W. van der. (2021). Crop yields in European agroforestry systems: A meta-analysis. Frontiers in Sustainable Food Systems, 5, 606631.
Jacobs, S. R., Webber, H., Niether, W., Grahmann, K., Lüttschwager, D., Schwartz, C., Breuer, L., & Bellingrath-Kimura, S. D. (2022). Modification of the microclimate and water balance through the integration of trees into temperate cropping systems. Agricultural and Forest Meteorology, 323, 109065. https://doi.org/10.1016/j.agrformet.2022.109065
Kanzler, M., Böhm, C., Mirck, J., Schmitt, D., & Veste, M. (2019). Microclimate effects on evaporation and winter wheat (Triticum aestivum L.) yield within a temperate agroforestry system. Agroforestry Systems, 93(5), 1821–1841. https://doi.org/10.1007/s10457-018-0289-4
Kort, J. (1988). 9. Benefits of windbreaks to field and forage crops. Proceedings of an International Symposium on Windbreak Technology, 22–23, 165–190. https://doi.org/10.1016/0167-8809(88)90017-5
Kruger, B. R., & Sackett, J. D. (2024). Abiotic and biotic factors influencing small-scale corn production along a shade spectrum in arid urban agriculture settings. PLOS ONE, 19(4), e0301633. https://doi.org/10.1371/journal.pone.0301633
Lenth, R. (2024). Emmeans: Estimated marginal means, aka least-squares means. R package version 1.10.3.
Luedeling, E., Smethurst, P. J., Baudron, F., Bayala, J., Huth, N. I., van Noordwijk, M., Ong, C. K., Mulia, R., Lusiana, B., Muthuri, C., & Sinclair, F. L. (2016). Field-scale modeling of tree–crop interactions: Challenges and development needs. Agricultural Systems, 142, 51–69. https://doi.org/10.1016/j.agsy.2015.11.005
Lüttger, A. B., & Feike, T. (2018). Development of heat and drought related extreme weather events and their effect on winter wheat yields in Germany. Theor. Appl. Climatol., 132, 15–29. https://doi.org/10.1007/s00704-017-2076-y
Majaura, M., Böhm, C., & Freese, D. (2024). The Influence of Trees on Crop Yields in Temperate Zone Alley Cropping Systems: A Review. Sustainability, 16(8). https://doi.org/10.3390/su16083301
Markonis, Y., Kumar, R., Hanel, M., Rakovec, O., Máca, P., & AghaKouchak, A. (2021). The rise of compound warm-season droughts in Europe. Science Advances, 7(6), eabb9668. https://doi.org/10.1126/sciadv.abb9668
Nakagawa, S., Johnson, P. C. D., & Schielzeth, H. (2017). The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. Journal of The Royal Society Interface, 14(134), 20170213. https://doi.org/10.1098/rsif.2017.0213
Pardon, P., Mertens, J., Reubens, B., Reheul, D., Coussement, T., Elsen, A., Nelissen, V., & Verheyen, K. (2020). Juglans regia (walnut) in temperate arable agroforestry systems: Effects on soil characteristics, arthropod diversity and crop yield. Renewable Agriculture and Food Systems, 35(5), 533–549. https://doi.org/10.1017/S1742170519000176
Pelton, W. L. (1967). THE EFFECT OF A WINDBREAK ON WIND TRAVEL, EVAPORATION AND WHEAT YIELD. Canadian Journal of Plant Science, 47(2), 209–214. https://doi.org/10.4141/cjps67-036
Piepho, H. P., & Edmondson, R. N. (2018). A tutorial on the statistical analysis of factorial experiments with qualitative and quantitative treatment factor levels. Journal of Agronomy and Crop Science, 204(5), 429–455. https://doi.org/10.1111/jac.12267
Quinkenstein, A., Wöllecke, J., Böhm, C., Grünewald, H., Freese, D., Schneider, B. U., & Hüttl, R. F. (2009). Ecological benefits of the alley cropping agroforestry system in sensitive regions of Europe. Sustainability Impact Assessment and Land-Use Policies for Sensitive Regions, 12(8), 1112–1121. https://doi.org/10.1016/j.envsci.2009.08.008
Reynolds, P. E., Simpson, J. A., Thevathasan, N. V., & Gordon, A. M. (2007). Effects of tree competition on corn and soybean photosynthesis, growth, and yield in a temperate tree-based agroforestry intercropping system in southern Ontario, Canada. Carbon Sequestration and Landscape Ecology in Western Europe, 29(4), 362–371. https://doi.org/10.1016/j.ecoleng.2006.09.024
Rivest, D., & Vézina, A. (2015). Maize yield patterns on the leeward side of tree windbreaks are site-specific and depend on rainfall conditions in eastern Canada. Agroforestry Systems, 89(2), 237–246. https://doi.org/10.1007/s10457-014-9758-6
Rosenberg, N. J., Blad, B. L., & Verma, S. B. (1983). Microclimate: The biological environment. John Wiley & Sons.
Schoeneberger, M., Bentrup, G., de Gooijer, H., Soolanayakanahally, R., Sauer, T., Brandle, J., Zhou, X., & Current, D. (2012). Branching out: Agroforestry as a climate change mitigation and adaptation tool for agriculture. Journal of Soil and Water Conservation, 67(5), 128A. https://doi.org/10.2489/jswc.67.5.128A
Searle, S. R., Speed, F. M., & Milliken, G. A. (1980). Population Marginal Means in the Linear Model: An Alternative to Least Squares Means. The American Statistician, 34(4), 216–221. https://doi.org/10.1080/00031305.1980.10483031
Stephenson, N. L. (1990). Climatic control of vegetation distribution: The role of the water balance. The American Naturalist, 135(5), 649–670.
Swieter, A., Langhof, M., & Lamerre, J. (2022). Competition, stress and benefits: Trees and crops in the transition zone of a temperate short rotation alley cropping agroforestry system. Journal of Agronomy and Crop Science, 208(2), 209–224. https://doi.org/10.1111/jac.12553
Swieter, A., Langhof, M., Lamerre, J., & Greef, J. M. (2019). Long-term yields of oilseed rape and winter wheat in a short rotation alley cropping agroforestry system. Agroforestry Systems, 93(5), 1853–1864. https://doi.org/10.1007/s10457-018-0288-5
van Noordwijk, M., Coe, R., Sinclair, F. L., Luedeling, E., Bayala, J., Muthuri, C. W., Cooper, P., Kindt, R., Duguma, L., Lamanna, C., & Minang, P. A. (2021). Climate change adaptation in and through agroforestry: Four decades of research initiated by Peter Huxley. Mitigation and Adaptation Strategies for Global Change, 26(5), 18. https://doi.org/10.1007/s11027-021-09954-5
Wang, Z., Wu, Y., Cao, Q., Shen, Y., & Zhang, B. (2021). Modeling the coupling processes of evapotranspiration and soil water balance in agroforestry systems. Agricultural Water Management, 250, 106839. https://doi.org/10.1016/j.agwat.2021.106839
Wolz, K. J., & DeLucia, E. H. (2018). Alley cropping: Global patterns of species composition and function. Agriculture, Ecosystems & Environment, 252, 61–68. https://doi.org/10.1016/j.agee.2017.10.005
Wolz, K. J., Lovell, S. T., Branham, B. E., Eddy, W. C., Keeley, K., Revord, R. S., Wander, M. M., Yang, W. H., & DeLucia, E. H. (2018). Frontiers in alley cropping: Transformative solutions for temperate agriculture. Global Change Biology, 24(3), 883–894. https://doi.org/10.1111/gcb.13986

Tables 1 and 2 are available in the Supplementary Files section.

Table1.png
Table 1 Effects of agroforestry variants, distance to trees, aspect and climatic water balance on winter crop yields in alley cropping agroforestry.* significant at alpha =0.05
Table2.png
Table 2 Model statistics in relation to a change in CWB obtained via the EMTRENDS function showing the effect on yields at different distance classes and aspect. CWB-trend shows the estimated yield change in t/ha with every 100 mm increase in CWB. WNW = west northwest, ESE = east southeast, df = 28100 * significant at alpha = 0.05

Download PDF

Reviewers agreed at journal
19 Aug, 2024
Reviewers invited by journal
14 Aug, 2024
Editor invited by journal
13 Aug, 2024
Editor assigned by journal
05 Aug, 2024
First submitted to journal
05 Aug, 2024

You are reading this latest preprint version

Sheltered by trees – long-term yield dynamics in temperate alley cropping agroforestry with changing water availability

Status:

Version 1

Abstract

Figures

1 Introduction

2 Materials and Methods

2.1 Study site / Experimental design

2.2 Data

2.3 Statistical analysis

3 Results and Discussion

3.1 Effects of alley cropping agroforestry on winter crop yields

3.2 Effects of water availability on yield in temperate alley cropping agroforestry

4 Conclusion

Declarations

References

Tables

Supplementary Files

Status:

Version 1