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Functionally diverse cover crops support ecological weed management in orchard cropping systems

Published online by Cambridge University Press:  15 January 2024

Steven Haring Open the ORCID record for Steven Haring [Opens in a new window] ,
Amélie C. M. Gaudin Open the ORCID record for Amélie C. M. Gaudin [Opens in a new window]  and
Bradley D. Hanson Open the ORCID record for Bradley D. Hanson [Opens in a new window]
Steven Haring*
Affiliation:
Department of Plant Sciences, University of California, Davis, CA 95616, USA
Amélie C. M. Gaudin
Affiliation:
Department of Plant Sciences, University of California, Davis, CA 95616, USA
Bradley D. Hanson
Affiliation:
Department of Plant Sciences, University of California, Davis, CA 95616, USA
*
Corresponding author: Steven Haring; Email: [email protected]
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Abstract

Diverse agricultural management practices are critical for agroecosystem sustainability, and cover crops provide opportunity for varied management and increased biodiversity. Understanding how cover crops fill open ecological niches underneath the trees, interact with weeds, and potentially provide ecosystem services to decrease pest pressure is essential for ecological agricultural management. The goal of this study was to test the weed suppression potential of two cover crop treatments with varied functional diversity compared to standard weed management practices in commercial almond orchards in California. Transect plant surveys were used to evaluate orchard plant communities under a functionally diverse seed mix including grasses, legumes, and brassicas, and a relatively uniform cover crop mix that included only brassica species. Winter annual orchard cover crops reduced bare ground from 39.3% of total land area to 15.9 or 11.4%, depending on treatment. Furthermore, winter cover crops displaced weeds with a negative correlation of 0.74. The presence of cover crops did not consistently affect weed community composition for low-richness weed communities found in California orchards. Diverse cover crop mixes more reliably resulted in increased ground cover across site years compared to uniform cover crop mixes, with coefficients of variation for ground cover at 49.6 and 91.5%, respectively. Cover crops with different levels of functional diversity can contribute to orchard weed management programs at commercial scales. Functional diversity supports cover crop establishment, abundance, and competitiveness across varied agroecological conditions, and cover crop mixes could be designed to address an assortment of orchard management concerns.

Keywords

Agrobiodiversityagroecologyintegrated pest managementorchardssustainable agriculturesustainable intensification
Type
Research Article
Information
Renewable Agriculture and Food Systems , Volume 38 , 2023 , e54
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Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

Introduction

Cover cropping is a management strategy which adds potentially beneficial biodiversity to agroecosystems. Depending on specific cover crop management practices (Bergtold et al., Reference Bergtold, Ramsey, Maddy and Williams2019), farmers may leverage planned biodiversity to enhance regulating ecosystem services (Beillouin et al., Reference Beillouin, Ben-Ari, Malézieux, Seufert and Makowski2021; McClelland, Paustian, and Schipanski, Reference McClelland, Paustian and Schipanski2021; Tamburini et al., Reference Tamburini, Bommarco, Wanger, Kremen, van der Heijden, Liebman and Hallin2020), increase cropping system resilience (Reiss and Drinkwater, Reference Reiss and Drinkwater2018; Renwick et al., Reference Renwick, Deen, Silva, Gilbert, Maxwell, Bowles and Gaudin2021), reduce agricultural externalities, and support sustainable intensification (Wittwer et al., Reference Wittwer, Dorn, Jossi and van der Heijden2017). In particular, cover crops can increase beneficial insect populations (English-Loeb et al., Reference English-Loeb, Rhainds, Martinson and Ugine2003), reduce pest insect populations (Bugg, Reference Bugg1992; Bugg and Waddington, Reference Bugg and Waddington1994), support several aspects of soil health (Romdhane et al., Reference Romdhane, Spor, Busset, Falchetto, Martin, Bizouard, Bru, Breuil, Philippot and Cordeau2019; Unger and Vigil, Reference Unger and Vigil1998), reduce soil erosion (Novara et al., Reference Novara, Gristina, Saladino, Santoro and Cerdà2011), reduce pollutants in agricultural runoff (Dabney, Delgado, and Reeves, Reference Dabney, Delgado and Reeves2001), increase crop yield stability (Gaudin et al., Reference Gaudin, Tolhurst, Ker, Janovicek, Tortora, Martin and Deen2015), increase farm profitability (Correia et al., Reference Correia, Brito, Sampaio, Dias, Bacelar, Gonçalves, Ferreira, Moutinho-Pereira and Rodrigues2015), sequester atmospheric carbon (Novara et al., Reference Novara, Minacapilli, Santoro, Rodrigo-Comino, Carrubba, Sarno, Venezia and Gristina2019), and otherwise increase the ecological value of farmland.

Whereas a large body of research has rigorously addressed the ecological impacts of annual cover crops grown in the fallow period between two annual cash crops (e.g., Teasdale, Beste, and Potts Reference Teasdale, Beste and Potts1991), there is less information about cover crop impacts in perennial systems. In contrast to cover crops in annual cropping systems, orchard cover crops are grown under the tree canopy and rely on spatial separation to avoid competition with the main cash crop. Cover crops have demonstrated impacts on abiotic factors in perennial systems, including improving soil structure (Ramos et al., Reference Ramos, Benítez, García and Robles2010; Walsh et al., Reference Walsh, MacKenzie, Salmins and Buszard1996), increasing soil nutrition (Sánchez et al., Reference Sánchez, Giayetto, Cichón, Fernández, Aruani and Curetti2007), increasing water use (Monteiro and Lopes, Reference Monteiro and Lopes2007), and reducing orchard temperature (O'Connell and Snyder, Reference O'Connell and Snyder1999). More research is needed to fully understand how the biotic functioning of orchard cover crops affects horticultural management.

Such research would support ecological systems-based approaches to agricultural sustainability, such as integrated pest (or weed) management, which rely on biodiversity and regulating ecosystem services to support multiple aspects of the agroecosystem (Haring, Reference Haring2021). Integrated pest management highlights the practical importance of basic ecological knowledge. For example, weeds can indicate the absence of unfilled ecological niches within the orchard system (Smith, Mortensen, and Ryan, Reference Smith, Mortensen and Ryan2010). Therefore, reducing available ecological niches (such as by planting predictable, domesticated plants that reduce resource availability) could displace weeds (Kruidhof, Bastiaans, and Kropff, Reference Kruidhof, Bastiaans and Kropff2008; Mirsky et al., Reference Mirsky, Curran, Mortenseny, Ryany and Shumway2011), suppress herbicide-resistant weeds (Bunchek et al., Reference Bunchek, Wallace, Curran, Mortensen, VanGessel and Scott2020; Pittman, Barney, and Flessner, Reference Pittman, Barney and Flessner2019), and provide additional sustainability benefits (Liebman and Davis, Reference Liebman and Davis2000). Whereas conventional orchards have significant unused resource pools that lead to the need for intensive vegetation control, cropping systems with diverse ground covers limit weed proliferation by regulating resource availability and safe sites for seed germination (Adeux et al., Reference Adeux, Vieren, Carlesi, Bàrberi, Munier-Jolain and Cordeau2019).

Winter annual cover crops have a life cycle that is coincidental with winter rains in Mediterranean climates as well as the dormant period of deciduous orchard trees (Baumgartner, Steenwerth, and Veilleux, Reference Baumgartner, Steenwerth and Veilleux2008; Bugg et al., Reference Bugg, McGourty, Sarrantonio, Lanini and Bartolucci1996; DeVincentis et al., Reference DeVincentis, Solis, Rice, Zaccaria, Snyder, Maskey, Gomes, Gaudin and Mitchell2022). This phenology allows winter annual weeds to have significant temporal niche differentiation compared to the orchard crop while allowing exploitative competition between cover crops and orchard weeds. Other forms of interference, such as allelopathy or suppression of summer weed germination with cover crop residues (Creamer et al., Reference Creamer, Bennett, Stinner, Cardina and Regnier1996; Putnam, Defrank, and Barnes, Reference Putnam, Defrank and Barnes1983) may also be regulating weed suppression potential of cover crop communities. The relative contribution of these mechanisms varies seasonally and over the life cycle of the orchard as changes in resource availability often alter the phenology of competition (Pearson, Ortega, and Maron, Reference Pearson, Ortega and Maron2017).

Life cycle is just one example of many functional traits that affect how the functional ecology of a cover crop species can support weed suppression, and other functions such as nutrient acquisition or germination ecology could be critical (Smith and Gross, Reference Smith and Gross2007). Many researchers have investigated the performance of cover crop mixes based on the functional traits of component species (Haramoto and Pearce, Reference Haramoto and Pearce2019; Kaye et al., Reference Kaye, Finney, White, Bradley, Schipanski, Alonso-Ayuso, Hunter, Burgess and Mejia2019; McKenzie-Gopsill et al., Reference McKenzie-Gopsill, Mills, MacDonald and Wyand2022; Ramírez-García et al., Reference Ramírez-García, Carrillo, Ruiz, Alonso-Ayuso and Quemada2015; Smith, Atwood, and Warren, Reference Smith, Atwood and Warren2014; Trichard et al., Reference Trichard, Alignier, Chauvel and Petit2013). By combining multiple species with complementary functional traits that match specific management goals, multispecies cover crops can enhance the resilience of an agroecosystem while providing several ecosystem services. Studies in unmanaged ecosystems likewise highlight the importance of diverse, multifunctional plant communities for reducing invasibility through mechanisms like niche differentiation (e.g., through resource partitioning and phenological differences) and variation in developmental biology and phenotypic plasticity (Levine and D'Antonio, Reference Levine and D'Antonio1999; Naeem et al., Reference Naeem, Knops, Tilman, Howe, Kennedy and Gale2000; Tilman, Wedin, and Knops, Reference Tilman, Wedin and Knops1996).

Despite a growing body of research on the functional ecology of multispecies cover crops, the relationships between cover crop species in a mix are frequently distinctive and unpredictable (Stefan, Engbersen, and Schöb, Reference Stefan, Engbersen and Schöb2021). There remains uncertainty in how these patterns emerge at scales relevant to intensive, commercial agricultural given increased disturbance and decreased species richness in such systems (Bybee-Finley, Mirsky, and Ryan, Reference Bybee-Finley, Mirsky and Ryan2017; Smith, Warren, and Cordeau, Reference Smith, Warren and Cordeau2020). More practical and hands-on information about the functional diversity of multispecies cover crop mixes at large scales could improve how orchard managers employ complementarity to design species mixes that balance multiple management goals. This study aims to fill a critical knowledge gap in understanding the ecological effects of multispecies cover crops when they are integrated into existing large-scale, intensified orchard cropping systems.

Our goal for this study was to determine how cover crop mixes with different levels of diversity perform within existing orchard management systems, and our experiment and analyses were selected to focus on plant communities across a range of variable, real-world conditions found in commercial almond orchards. We hypothesize that both functionally uniform and diverse cover crop mixes can provide living ground cover that displaces weeds, but that functionally diverse cover crops will emerge and compete for resources more consistently across growing seasons and locations and are therefore better able to impact weed community composition. To evaluate these hypotheses, we examined four indicators of cover crop function: (1) area of bare ground underneath cover crops and weedy resident vegetation, (2) relative ground cover of cover crops and weeds, (3) stability of ground cover provided by cover crops over space and time, and (4) impacts on weed communities.

Materials and methods

We evaluated plant communities under two multi-species cover crop mixes, one that is functionally diverse and one that is functionally uniform, over two seasons in three commercial almond (Prunis dulcis (Mill.) D.A. Webb) orchards in the Central Valley of California. We used two different cover crop mixtures that were designed to fulfill different agroecological goals within almond orchards. Namely, we used a mix with high functional diversity that is used commercially for improving soil structure and limiting soil erosion because its constituent species come from plant families with contrasting root architecture among other characteristics, as well as a mix with lower functional diversity that is used for providing floral resources to foraging pollinators with species from one plant family that have overlapping flowering phenology. We evaluated their impact on weed population density and species communities across a wide geographical area in central California.

Experimental design and management

We created replicated, large-plot experiments in commercial almond orchards in Tehama, Merced, and Kern Counties in California (Table 1). These locations span nearly 600 km in the Central Valley of California. This region produces over 99% of the almonds in the United States (Anonymous, 2020). The experiment used a randomized block design with four replicates of three or four different ground cover treatments at each site. Ground cover treatments were implemented for two years on the same plots, beginning in the fall of 2017 and ending in the late summer of 2019 (Table 2). These treatments represented commercial-standard management practices and two different five-species cover crop mixes with different levels of functional diversity. Plots were 25.5 m wide at each site, encompassing four orchard alleys and three tree rows with another tree row between plots. In the perpendicular direction, plots extended the entire length of the orchard (195 m at the Tehama site, 385 m at the Merced site, and 320 m at the Kern site).

Table 1. Description of the three commercial almond orchards used as experimental sites in this study

Table 2. Description and dates of cover crop management actions at each of the three commercial almond orchards over two years in this study

The study was designed to use commercially relevant spatial and temporal scales, and orchard management was determined by grower cooperators for agronomic relevance. All orchards were equipped with microsprinkler irrigation, and irrigation schedules were determined based on almond evapotranspiration models in accordance with local weather conditions and recommendations (Fereres and Goldhamer, Reference Fereres and Goldhamer2003). Irrigation, insecticide, fungicide, and fertilizer treatments and rates were determined by each grower and applied to the tree rows only. Tree rows were maintained with conventional herbicide programs to create vegetation-free zones at the base of trees. These herbicide applications were performed with shielded sprayers at each site, and we did not observe any herbicide injury that would indicate herbicide drift to the cover crops throughout the experiment. Each of the sites was subjected to regular traffic from machinery and farmworkers to complete normal orchard management operations throughout the cover crop growing season.

Ground cover treatments

Two different winter cover crop mixes were planted in orchard alleyways and compared against two different control treatments that reflect mainstream orchard vegetation management programs (Table 3). These cover crop mixes were selected because of their current commercial availability in California and their contrasting functional diversity. Both mixes have the same number of species but represent different levels of functional diversity, with the uniform mix having five quite similar species and the diverse mix having species from several plant families and contrasting biological characteristics. Each mix was established at rates and with methods based on recommendations from the seed supplier.

Table 3. Description of the two cover crop and two commercial standard ground cover treatments evaluated in this experiment

The ‘uniform’ mix consisted of five functionally similar species, and this mix is used commercially in California and distributed as ‘PAm Mustard Mix’ by the Project Apis m. (Salt Lake City, UT, USA) Seeds For Bees program because its component species provide abundant floral resources for pollinators but slightly different flowering phenologies. The ‘diverse’ mix consisted of five species from the grass, brassica, and legume groups that are commonly used together in functionally diverse cover crop mixes (Altieri et al., Reference Altieri, Lana, Bittencourt, Kieling, Comin and Lovato2011) to support soil health by providing abundant biomass, multiple kinds of root architectures, and complementary resource needs.

The ‘resident’ vegetation treatment involved winter vegetation management primarily with mowing. The ‘bare’ treatment involved more intensive vegetation management, including one to two broadcast herbicide applications in the winter. For the bare treatment, herbicide applications timing and product choice were determined by grower cooperators but included broad spectrum herbicides without residual activity, which are common in California almond production, including glyphosate and glufosinate. The Tehama site included only the resident treatment due to the preference of our cooperator and to better reflect standard practices in this region of California which has more abundant winter rainfall. The Merced and Kern sites featured both the resident and bare treatments to better reflect high-intensity production systems in these regions.

Data collection

Orchard alley plant communities were evaluated with point-intercept transects. Transect surveys coincided with cover crop flowering for most species as well as winter weed flowering for many endemic species in the study area (dates are in Table 2). Each plot was surveyed with a single 50 m long transect with points observed evenly at each meter along the transect, and observations from all 50 points were combined to estimate plant cover across each plot. Transects were placed in the same location in each plot, with the starting point located 75 m from the end of the plot and inside the second tree row from the side of the plot. Transects extended diagonally across a single orchard alley, starting and ending on opposite edges of the planted swath.

At each point along the transect, plant cover was observed for the top layer of vegetation as observed from above, which varied depending on the height of target species. The observed vegetation type (plant species or bare ground) was recorded. Plants were identified to species visually in the field, except in the case of the white and yellow mustards in the uniform mix which were identified as one operational taxonomic unit due to morphological similarities at the observed growth stage, and the observed plant species or bare ground was recorded. The total number of occurrences of each vegetation type along each transect were summed and converted to a percentage to estimate ground cover in each plot. Transects described plant community composition for each plot, as well as bare ground (the portion of each transect that was not covered by any kind of vegetation) and ground cover from cover crops or weeds (found by counting the number of occurrences of all of the species associated with each vegetation type).

Statistical analysis

Analyses were performed in R 4.2.3 (R Core Team, 2023). To evaluate the first objective (evaluating ground cover and reduction of bare ground), comparisons of bare ground among treatments were made with ANOVA. ANOVA assumptions were inspected visually with qqPlot from the car package (Fox and Weisberg, Reference Fox and Weisberg2019). We detected a heavy tailed distribution and subsequently repeated the analyses using an arcsine square root transformed response variable as well as with a generalized linear model with a Poisson distribution. However, results were similar among the different analyses, and we display results from the untransformed ANOVA below. One outlier was identified with the Bonferroni outlier test using outlierTest. This outlier value was excluded from further analyses because it was collected in the same plot at the Merced site that had been previously excluded because it had not been planted in 2017 (i.e., no data from this plot from either study year were included). The model we used included these fixed effects as predictors: treatment, year, site, the interaction between year and site, and replicate nested within site. ANOVA was performed with Anova from the car package using type II sums of squares. Multiple comparisons were made with least-squares means using the emmeans package (Lenth, Reference Lenth2021).

For the second objective (evaluating tradeoffs in relative ground cover between cover crops and weeds), we evaluated the general relationship between cover crop and weed cover within cover crop treatments (i.e., not including the bare or resident treatments) across sites and years in this study. The relative ground cover from cover crops and weeds were modeled with the lm function in base R, and we used Anova for hypothesis testing. We used weed cover as the response variable and cover crop cover and cover crop treatment as predictors.

To evaluate the third objective (evaluating stability of cover crop cover over space and time), we assessed cover crop stability by comparing coefficients of variation for cover of each cover crop mix as pooled across sites and years in this study. Pooled coefficients of variation and their 95% confidence intervals were calculated with the ci.cv function in the MBESS package (Kelley, Reference Kelley2022) before they were compared with the modified signed-likelihood ratio test as implemented in the cvequality package (Marwick and Krishnamoorthy, Reference Marwick and Krishnamoorthy2019). These comparisons are based on recommendations by Reiss and Drinkwater (Reference Reiss and Drinkwater2018). For the final objective (evaluating impacts on weed communities), weed communities in the different cover crop treatments were analyzed with nonmetric multidimensional scaling (NMDS). We evaluated weed community groupings based on treatment, year, and site, as well as treatment and year within each site. NMDS was based on Bray–Curtis dissimilarity and was calculated using the metaMDS function in the vegan package (Oksanen et al., Reference Oksanen, Blanchet, Friendly, Kindt, Legendre, McGlinn, Minchin, O'Hara, Simpson, Solymos, Stevens, Szoecs and Wagner2020). We evaluated grouping variables using anosim, also from vegan, with 9999 permutations.

Results

Bare ground

Cover crop treatment (F 3,65 = 93.23, P < 0.001), site (F 2,65 = 30.21, P < 0.001), and their interaction (F 5,65 = 10.56, P < 0.001) had significant effects on the amount of bare ground observed in orchard alleys, while year (F 1,65 = 1.34, P = 0.251) and block (F 9,65 = 0.84, P = 0.582) did not (Fig. 1). Overall, the uniform and diverse mixes resulted in similar levels of bare ground (P = 0.289), at 15.9 ± 3.09 and 11.4 ± 2.89%, respectively, when averaged across sites and years. These values are less than the 39.3 ± 2.89% bare ground in the resident vegetation treatment (P < 0.001 for both comparisons).

Figure 1. Impacts of various four cover crop treatments on amount of uncovered ground soils in orchard alleyways (2018 and 2019). Two cover crop mixes (one consisting of functionally diverse species and the other consisting of uniform species) were compared against two commercially standard orchard management treatments (a treatment accommodated some resident vegetation and a higher intensity treatment to maintain bare ground) in three commercial orchards in Kern, Merced, and Tehama Counties, California, USA (the bare treatment was not included at the Tehama County site). The center line represents median, hinges represent first and third quartiles, and whiskers represent minimum and maximum values within 150% of the interquartile range.

Within the Kern site, each of the cover crop treatments resulted in significantly different (P = 0.003) levels of bare ground from one another, with the diverse mix (6.0 ± 4.64%) resulting in less bare ground than the uniform mix (28.0 ± 4.64%). Within the Merced site, the diverse mix (25.0 ± 6.55%) resulted in a similar level of bare ground to both the uniform mix (14.8 ± 7.88%, P = 0.332) and the resident vegetation treatment (36.5 ± 6.55%, P = 0.228). The Tehama site had low levels of bare ground across all three treatments, which were similar to one another (P > 0.25 for all comparisons). Both cover crop mixes had somewhat differing performance within sites, but both cover crop mixes on average reduced bare ground compared to standard commercial management practices.

Weed and cover crop cover

Across this study, cover crop cover was negatively associated with weed cover (Fig. 2; slope = −0.74, R 2 = 0.83, P < 0.001). When including cover crop treatment as a predictor, we found that cover crop cover was significant (F 1,43 = 176.72, P < 0.001) while cover crop treatment was not (F 1,43 = 0.35, P = 0.56). Regardless of the cover crop mix, we observed that increased ground cover from cover crops resulted in reduced ground cover from weeds. This relationship was described by a line with a slope less steep than negative one, indicating that every increase in the cover crop canopy covered some amount of bare ground in addition to the displaced weed vegetation.

Figure 2. Relationship between cover crop and weed cover in orchard alleyways (2018 and 2019). The line displays marginal replacement of each vegetation type relative to the other as determined by a linear model. Point shapes represent two different cover crop species mixes (one consisting of functionally diverse species and the other consisting of uniform species), though cover crop treatment was not a predictor of weed cover (P = 0.56) and was not included in linear model displayed here.

Cover crop stability

The coefficient of variation for cover crop cover from the diverse mix was 48.6%, significantly less variation than the 91.5% variation observed in the uniform mix (Fig. 3; P = 0.035). Across the experiment, the diverse mix resulted in more consistent levels of ground cover than the uniform mix, reliably creating ground cover across a range of geographical, environmental, and management-related variation.

Figure 3. Stability of cover crop cover in orchard alleyways for two cover crop mixes (one consisting of functionally diverse species and the other consisting of uniform species) (2018 and 2019). Points show the average coefficient of variation across six site-years in this study, and bars show 95% confidence intervals. The diverse cover crop mix exhibited less variation in ground cover compared to the uniform mix (P = 0.035).

Weed communities

The Tehama site had three to five times greater species richness than either of the other sites (Table 4). All the sites were dominated by small-statured, winter annual weed species such as annual bluegrass (Poa annua L.) and common chickweed (Stellaria media (L.) Vill.). The remainder of the species at all sites included both grass and broadleaf (dicotyledonous) species, with some additional summer annual and perennial species present at the Tehama site. Cover crops influenced weed communities but to different extents depending on the site and year. When using ANOSIM to test whether weed communities had similar constituent species (stress = 0.166), we found that weed communities were strongly associated with their site (R = 0.569, P < 0.001) and cover crop treatments (R = 0.091, P = 0.005). However, effect sizes were generally small and clusters can be difficult to identify visually when weed communities from each plot were plotted in nonmetric scaled space (Fig. 4). As described above, the differences between weed community diversity at each site were relatively clear, while there were few noticeable differences between weed communities in different cover crop treatments.

Table 4. Weed species found at each site over the course of the experiment

Species are listed in order of prevalence, based on the cumulative total number of observations at each site.

Figure 4. Ordination plots representing weed communities in orchard alleyways (2018 and 2019). Two cover crop mixes (one consisting of functionally diverse species and the other consisting of uniform species) were compared against two commercially standard orchard management treatments (a treatment accommodated some resident vegetation and a higher intensity treatment to maintain bare ground) in three commercial orchards in Kern, Merced, and Tehama Counties, California, USA (the bare treatment was not included at the Tehama County site). Plots were created with nonmetric multidimensional scaling. Each panel was created with from the same ordination analysis but displays points from only one site.

Because of the inherent differences between weed communities at each site, we also analyzed similarity of weed communities within each site individually. Weed communities were similar across years and treatments at Merced, and weed communities remained relatively sparse and homogenous throughout the experiment at that site (stress = 0.119). At the Tehama site, weed communities differed across years (R = 0.981, P < 0.001; stress = 0.150), which is logical given that cover crop establishment was very strong in 2018 to the point that we observed no weeds in the cover crop treatments that year. Weed communities differed with cover crop treatment at the Kern site (R = 0.316, P < 0.001; stress = 0.036), though we observed few qualitative differences in weed communities at the Kern site.

Discussion

Orchard cover crop mixes, as implemented in this study, were effective at establishing, reducing bare ground, and suppressing weeds. However, these effects were variable, and there is little evidence that the cover crop mixes we used had fixed impacts on the composition of orchard weed communities. Differences among sites, which could include climate and management factors, contributed to some of this variability. Our goal was to observe cover crops as they would be implemented by commercial almond growers in California, which entailed a variety of unique management decisions at each site. Growers will always implement some level of site-specific management that could affect cover crop performance, but it remains encouraging that cover crops that are sold and used commercially for different purposes could result in weed suppression across the three locations of the experiment. The diverse mix resulted in more consistent ground cover in this study, and ground cover led to greater weed suppression. However, cover crop cover, not the specific cover crop treatment, was the primary driver for weed suppression and other effects on weeds in this study.

Cover crop abundance

This study demonstrates the critical importance of cover crop abundance for weed competition. As hypothesized, the diverse cover crop mix more reliably created an abundant cover crop compared to the uniform cover crop mix. However, the diverse cover crop mix was not inherently more competitive, and both cover crop mixes suppressed weeds when abundant. The present study is currently the largest-scale study focusing on weed suppression and cover crop mixes in orchard systems and demonstrates that weed-suppressing cover crops can be integrated into existing commercial production systems.

The importance of cover crop abundance is consistent with numerous existing studies of weed suppressing cover crops in annual cropping systems (Bybee-Finley, Mirsky, and Ryan, Reference Bybee-Finley, Mirsky and Ryan2017; Creamer et al., Reference Creamer, Bennett, Stinner, Cardina and Regnier1996; Florence et al., Reference Florence, Higley, Drijber, Francis and Lindquist2019; MacLaren et al., Reference MacLaren, Swanepoel, Bennett, Wright and Dehnen-Schmutz2019; Smith, Warren, and Cordeau, Reference Smith, Warren and Cordeau2020). It is important to note that these studies primarily observed plant abundance by measuring cover crop and weed biomass, while the present study came to a similar conclusion by measuring abundance through ground cover. While cover and biomass are distinct from one another, both are useful measures of plant abundance (Wright, Reference Wright1991). The present study confirms through field experiments that ground cover is an important measure of cover crop abundance, which can support higher-throughput, non-destructive cover crop research compared to previous studies that rely on biomass collection.

Previous research has established that an abundant and competitive cover crop is likely to have features that contribute to weed suppression as well as other ecosystem services. For example, large and abundant cover crops are effective in exploitative competition due to asymmetric resource acquisition, such as root competition for soil nutrients (Weiner, Reference Weiner1990). Large and abundant root systems can also improve soil structure and increase soil organic matter (Pierret et al., Reference Pierret, Maeght, Clément, Montoroi, Hartmann and Gonkhamdee2016) or create traps for pest insects while creating safe sites for beneficial insects (Hassanali et al., Reference Hassanali, Herren, Khan, Pickett and Woodcock2008). This abundance could also cause other challenges for orchard managers. For example, cereal rye was included in the multifunctional mix in this study and is known to be an important component species for weed suppression (Akemo, Regnier, and Bennett, Reference Akemo, Regnier and Bennett2000; Barnes and Putnam, Reference Barnes and Putnam1983). However, large amounts of cereal rye residue are frequently reported by nut growers as a concern because of potential interference with on-ground nut harvest. While this experiment focused on winter management, and thus we did not evaluate almond yield, future development of orchard cover crops should aim to identify cover crop termination strategies that create acceptable conditions for nut harvest in the summer.

Cover crops and plant diversity

This study highlights that cover crop mixes with different levels of functional diversity can successfully support weed management across different orchard contexts. The addition of cover crop mixes in this study contributed significantly to orchard plant diversity, essentially doubling species richness in the mature orchards (Merced and Kern sites). Weed species richness was highest in the young orchard (Tehama site), where the orchard floor was relatively unshaded and still populated with many weed species carried over from the previous pasture system. This study implemented cover crops on a time scale relevant for adoption in contemporary orchards, but more research is needed to understand the cumulative effects of cover crop competition on weed community assembly over the decades-long lifespans of commercial orchards. Maintaining biodiversity is a major challenge for agroecosystems. While there is ongoing conflict between promoting functional diversity and achieving some vegetation management goals, this study reinforces the idea that cover crops are flexible tools that can support multiple management goals if planned appropriately (Crézé and Horwath, Reference Crézé and Horwath2021; De Leijster et al., Reference De Leijster, Santos, Wassen, Ramos-Font, Robles, Díaz, Staal and Verweij2019; Mia et al., Reference Mia, Massetani, Murri and Neri2020).

Cover crop mixes in this study were primarily selected for their existing commercial uses, which are intended to address almond management goals other than weed suppression. In this study, the diverse mix provided functional diversity that led to more stability. We intentionally focused on the end performance of existing multifunctional cover crops, but optimization of cover crop mixes through agronomic management programs and additional species selection could improve weed suppression as well as other ecosystem services (Haring and Hanson, Reference Haring and Hanson2022). Multifunctional cover crop mixes could be improved by additional recognition of the specific ecological relationships between constituent species and their resulting agroecosystem services (Baraibar et al., Reference Baraibar, Hunter, Schipanski, Hamilton and Mortensen2018; Finney and Kaye, Reference Finney and Kaye2017; Ingels et al., Reference Ingels, Horn, Bugg and Miller1994; Schipanski et al., Reference Schipanski, Barbercheck, Douglas, Finney, Haider, Kaye, Kemanian, Mortensen, Ryan, Tooker and White2014). Pest management is generally used to reduce biodiversity in cropping systems, but cover crops provide an opportunity to support pest management goals while simultaneously promoting biodiversity through functional vegetation management. Agricultural systems are designed to support ample plant growth, and this resource-rich environment could be more practical and efficient if it supported competitive, complementary, and useful biodiversity instead of unwanted weedy plants.

Data availability statement

Data are available from https://doi.org/10.25338/B8S63T

Acknowledgments

The authors gratefully acknowledge the contributions of our collaborators throughout this field project: Cynthia Crézé, Mae Culumber, Kent Daane, Amanda K Hodson, Danielle M Lightle, Jeffrey Mitchell, Andreas Westphal, Houston Wilson, Mohammad Yaghmour, and Cameron At Zuber. We also acknowledge the grower-cooperators that supported this project: Steve Gruenwald, Castle Farms, and Wegis & Young. This research took place on lands that are the ancestral homes of the Nomlaki, Patwin, and Yokuts peoples, and these peoples remain committed to the stewardship of these lands today.

Author contributions

All authors developed hypotheses, contributed to experimental design, and critically revised the manuscript. S. H. led field data collection, data analysis, and manuscript drafting. Stakeholders, including orchard growers and cooperative extension professionals, were included in project design and management, and specific parties are listed in the acknowledgements.

Funding statement

The Almond Board of California funded this research under project numbers 16-STEWCROP7 and 18-HORT12.

Competing interests

We have no conflicts interest to declare.

References

Adeux, G., Vieren, E., Carlesi, S., Bàrberi, P., Munier-Jolain, N. and Cordeau, S. (2019) ‘Mitigating crop yield losses through weed diversity’, Nature Sustainability, 2(11), pp. 1018–26. https://doi.org/10.1038/s41893-019-0415-yCrossRefGoogle Scholar
Akemo, M.C., Regnier, E.E. and Bennett, M.A. (2000) ‘Weed suppression in spring-sown rye (Secale cereale): Pea (Pisum sativum) cover crop mixes’, Weed Technology, 14(3), pp. 545–9.CrossRefGoogle Scholar
Altieri, M.A., Lana, M.A., Bittencourt, H.V., Kieling, A.S., Comin, J.J. and Lovato, P.E. (2011) ‘Enhancing crop productivity via weed suppression in organic no-till cropping systems in Santa Catarina, Brazil’, Journal of Sustainable Agriculture, 35(8), pp. 855–69. https://doi.org/10.1080/10440046.2011.588998CrossRefGoogle Scholar
Anonymous. (2020) California agricultural statistics review, 2018-2019. California Department of Food and Agriculture. Available at: https://www.cdfa.ca.gov/statistics/PDFs/2018-2019AgReportnass.pdfGoogle Scholar
Baraibar, B., Hunter, M.C., Schipanski, M.E., Hamilton, A. and Mortensen, D.A. (2018) ‘Weed suppression in cover crop monocultures and mixtures’, Weed Science, 66(1), pp. 121–33. https://doi.org/10.1017/wsc.2017.59CrossRefGoogle Scholar
Barnes, J.P. and Putnam, A.R. (1983) ‘Rye residues contribute weed suppression in no-tillage cropping systems’, Journal of Chemical Ecology, 9(8), pp. 1045–57. https://doi.org/10.1007/BF00982210CrossRefGoogle ScholarPubMed
Baumgartner, K., Steenwerth, K.L. and Veilleux, L. (2008) ‘Cover-crop systems affect weed communities in a California vineyard’, Weed Science, 56(04), pp. 596–605. https://doi.org/10.1614/WS-07-181.1CrossRefGoogle Scholar
Beillouin, D., Ben-Ari, T., Malézieux, E., Seufert, V. and Makowski, D. (2021) ‘Positive but variable effects of crop diversification on biodiversity and ecosystem services’, Global Change Biology, 27(19), pp. 4697–710. https://doi.org/10.1111/gcb.15747CrossRefGoogle ScholarPubMed
Bergtold, J.S., Ramsey, S., Maddy, L. and Williams, J.R. (2019) ‘A review of economic considerations for cover crops as a conservation practice’, Renewable Agriculture and Food Systems, 34(1), pp. 62–76. https://doi.org/10.1017/S1742170517000278CrossRefGoogle Scholar
Bugg, R.L. (1992) ‘Using cover crops to manage arthropods on truck farms’, HortScience, 27(7), pp. 741–5. https://doi.org/10.21273/HORTSCI.27.7.741CrossRefGoogle Scholar
Bugg, R.L., McGourty, G., Sarrantonio, M., Lanini, W.T. and Bartolucci, R. (1996) ‘Comparison of 32 cover crops in an organic vineyard on the North Coast of California’, Biological Agriculture & Horticulture, 13(1), pp. 63–81. https://doi.org/10.1080/01448765.1996.9754766CrossRefGoogle Scholar
Bugg, R.L. and Waddington, C. (1994) ‘Using cover crops to manage arthropod pests of orchards: a review’, Agriculture, Ecosystems & Environment, 50(1), pp. 11–28. https://doi.org/10.1016/0167-8809(94)90121-XCrossRefGoogle Scholar
Bunchek, J.M., Wallace, J.M., Curran, W.S., Mortensen, D.A., VanGessel, M.J. and Scott, B.A. (2020) ‘Alternative performance targets for integrating cover crops as a proactive herbicide-resistance management tool’, Weed Science, 68(5), pp. 534–44. https://doi.org/10.1017/wsc.2020.49CrossRefGoogle Scholar
Bybee-Finley, K.A., Mirsky, S.B. and Ryan, M.R. (2017) ‘Crop biomass not species richness drives weed suppression in warm-season annual grass–legume intercrops in the northeast’, Weed Science, 65(5), pp. 669–80. https://doi.org/10.1017/wsc.2017.25CrossRefGoogle Scholar
Correia, C.M., Brito, C., Sampaio, A., Dias, A.A., Bacelar, E., Gonçalves, B., Ferreira, H., Moutinho-Pereira, J. and Rodrigues, M.A. (2015) ‘Leguminous cover crops improve the profitability and the sustainability of rainfed olive (Olea europaea l.) orchards: from soil biology to physiology of yield determination’, Procedia Environmental Sciences, 29, pp. 282–3. https://doi.org/10.1016/j.proenv.2015.07.213CrossRefGoogle Scholar
Creamer, N.G., Bennett, M.A., Stinner, B.R., Cardina, J. and Regnier, E.E. (1996) ‘Mechanisms of weed suppression in cover crop-based production systems’, HortScience, 31(3), pp. 410–3. https://doi.org/10.21273/HORTSCI.31.3.410CrossRefGoogle Scholar
Crézé, C.M. and Horwath, W.R. (2021) ‘Cover cropping: a malleable solution for sustainable agriculture? Meta-analysis of ecosystem service frameworks in perennial systems’, Agronomy, 11(5), Article 862. https://doi.org/10.3390/agronomy11050862CrossRefGoogle Scholar
Dabney, S.M., Delgado, J.A. and Reeves, D.W. (2001) ‘Using winter cover crops to improve soil and water quality’, Communications in Soil Science and Plant Analysis, 32(7–8), pp. 1221–50. https://doi.org/10.1081/CSS-100104110CrossRefGoogle Scholar
De Leijster, V., Santos, M.J., Wassen, M.J., Ramos-Font, M.E., Robles, A.B., Díaz, M., Staal, M. and Verweij, P.A. (2019) ‘Agroecological management improves ecosystem services in almond orchards within one year’, Ecosystem Services, 38, p. 100948. https://doi.org/10.1016/j.ecoser.2019.100948CrossRefGoogle Scholar
DeVincentis, A., Solis, S., Rice, S., Zaccaria, D., Snyder, R., Maskey, M., Gomes, A., Gaudin, A. and Mitchell, J. (2022) ‘Impacts of winter cover cropping on soil moisture and evapotranspiration in California's specialty crop fields may be minimal during winter months’, California Agriculture, 76(1), pp. 37–45.CrossRefGoogle Scholar
English-Loeb, G., Rhainds, M., Martinson, T. and Ugine, T. (2003) ‘Influence of flowering cover crops on Anagrus parasitoids (Hymenoptera: Mymaridae) and Erythroneura leafhoppers (Homoptera: Cicadellidae) in New York vineyards’, Agricultural and Forest Entomology, 5(2), pp. 173–81. https://doi.org/10.1046/j.1461-9563.2003.00179.xCrossRefGoogle Scholar
Fereres, E. and Goldhamer, D. (2003) ‘Suitability of stem diameter variations and water potential as indicators for irrigation scheduling of almond trees’, The Journal of Horticultural Science and Biotechnology, 78(2), pp. 139–44. https://doi.org/10.1080/14620316.2003.11511596CrossRefGoogle Scholar
Finney, D.M. and Kaye, J.P. (2017) ‘Functional diversity in cover crop polycultures increases multifunctionality of an agricultural system’, Journal of Applied Ecology, 54(2), pp. 509–17. https://doi.org/10.1111/1365-2664.12765CrossRefGoogle Scholar
Florence, A.M., Higley, L.G., Drijber, R.A., Francis, C.A. and Lindquist, J.L. (2019) ‘Cover crop mixture diversity, biomass productivity, weed suppression, and stability’, PLoS ONE, 14(3), p. e0206195. https://doi.org/10.1371/journal.pone.0206195CrossRefGoogle ScholarPubMed
Fox, J. and Weisberg, S. (2019) An R companion to applied regression. 3rd edn. Thousand Oaks, CA: Sage. Available at: https://socialsciences.mcmaster.ca/jfox/Books/Companion/Google Scholar
Gaudin, A.C.M., Tolhurst, T.N., Ker, A.P., Janovicek, K., Tortora, C., Martin, R.C. and Deen, W. (2015) ‘Increasing crop diversity mitigates weather variations and improves yield stability’, PLoS ONE, 10(2), p. e0113261. https://doi.org/10.1371/journal.pone.0113261CrossRefGoogle ScholarPubMed
Haramoto, E.R. and Pearce, R. (2019) ‘Cover crop termination treatment impacts weed suppression potential’, Weed Science, 67(1), pp. 91–102. https://doi.org/10.1017/wsc.2018.75CrossRefGoogle Scholar
Haring, S.C. (2021) ‘A progressive approach for integrated pest management’, Weed Science, 69(2), pp. 129–31. https://doi.org/10.1017/wsc.2020.94CrossRefGoogle Scholar
Haring, S.C. and Hanson, B.D. (2022) ‘Agronomic cover crop management supports weed suppression and competition in California orchards’, Weed Science, 70(5), pp. 595–602. https://doi.org/10.1017/wsc.2022.48CrossRefGoogle Scholar
Hassanali, A., Herren, H., Khan, Z.R., Pickett, J.A. and Woodcock, C.M. (2008) ‘Integrated pest management: the push-pull approach for controlling insect pests and weeds of cereals, and its potential for other agricultural systems including animal husbandry’, Philosophical Transactions of the Royal Society B: Biological Sciences, 363(1491), pp. 611–21. https://doi.org/10.1098/rstb.2007.2173CrossRefGoogle ScholarPubMed
Ingels, C., Horn, M., Bugg, R. and Miller, P. (1994) ‘Selecting the right cover crop gives multiple benefits’, California Agriculture, 48(5), pp. 43–8.CrossRefGoogle Scholar
Kaye, J., Finney, D., White, C., Bradley, B., Schipanski, M., Alonso-Ayuso, M., Hunter, M., Burgess, M. and Mejia, C. (2019) ‘Managing nitrogen through cover crop species selection in the U.S. mid-Atlantic’, PLoS ONE, 14(4), p. e0215448. https://doi.org/10.1371/journal.pone.0215448CrossRefGoogle ScholarPubMed
Kelley, K. (2022) MBESS: The MBESS R Package. Available at: https://CRAN.R-project.org/package=MBESSGoogle Scholar
Kruidhof, H.M., Bastiaans, L. and Kropff, M.J. (2008) ‘Ecological weed management by cover cropping: effects on weed growth in autumn and weed establishment in spring’, Weed Research, 48(6), pp. 492–502. https://doi.org/10.1111/j.1365-3180.2008.00665.xCrossRefGoogle Scholar
Lenth, R.V. (2021) emmeans: Estimated Marginal Means, aka Least-Squares Means. Available at: https://CRAN.R-project.org/package=emmeansGoogle Scholar
Levine, J.M. and D'Antonio, C.M. (1999) ‘Elton revisited: a review of evidence linking diversity and invasibility’, Oikos, 87(1), pp. 15–26. https://doi.org/10.2307/3546992CrossRefGoogle Scholar
Liebman, M. and Davis, A.S. (2000) ‘Integration of soil, crop and weed management in low-external-input farming systems’, Weed Research, 40(1), pp. 27–47. https://doi.org/10.1046/j.1365-3180.2000.00164.xCrossRefGoogle Scholar
MacLaren, C., Swanepoel, P., Bennett, J., Wright, J. and Dehnen-Schmutz, K. (2019) ‘Cover crop biomass production is more important than diversity for weed suppression’, Crop Science, 59(2), pp. 733–48. https://doi.org/10.2135/cropsci2018.05.0329CrossRefGoogle Scholar
Marwick, B. and Krishnamoorthy, K. (2019) cvequality: Tests for the Equality of Coefficients of Variation from Multiple Groups. Available at: https://CRAN.R-project.org/package=cvequalityGoogle Scholar
McClelland, S.C., Paustian, K. and Schipanski, M.E. (2021) ‘Management of cover crops in temperate climates influences soil organic carbon stocks: a meta-analysis’, Ecological Applications, 31(3), p. e02278. https://doi.org/10.1002/eap.2278CrossRefGoogle ScholarPubMed
McKenzie-Gopsill, A., Mills, A., MacDonald, A.N. and Wyand, S. (2022) ‘The importance of species selection in cover crop mixture design’, Weed Science, 70(4), pp. 436–47. https://doi.org/10.1017/wsc.2022.28CrossRefGoogle Scholar
Mia, M.J., Massetani, F., Murri, G. and Neri, D. (2020) ‘Sustainable alternatives to chemicals for weed control in the orchard—a review’, Horticultural Science, 47(1), pp. 1–12. https://doi.org/10.17221/29/2019-HORTSCICrossRefGoogle Scholar
Mirsky, S.B., Curran, W.S., Mortenseny, D.M., Ryany, M.R. and Shumway, D.L. (2011) ‘Timing of cover-crop management effects on weed suppression in no-till planted soybean using a roller-crimper’, Weed Science, 59(3), pp. 380–9. https://doi.org/10.1614/WS-D-10-00101.1CrossRefGoogle Scholar
Monteiro, A. and Lopes, C.M. (2007) ‘Influence of cover crop on water use and performance of vineyard in Mediterranean Portugal’, Agriculture, Ecosystems & Environment, 121(4), pp. 336–42. https://doi.org/10.1016/j.agee.2006.11.016CrossRefGoogle Scholar
Naeem, S., Knops, J.M.H., Tilman, D., Howe, K.M., Kennedy, T. and Gale, S. (2000) ‘Plant diversity increases resistance to invasion in the absence of covarying extrinsic factors’, Oikos, 91(1), pp. 97–108. https://doi.org/10.1034/j.1600-0706.2000.910108.xCrossRefGoogle Scholar
Novara, A., Gristina, L., Saladino, S.S., Santoro, A. and Cerdà, A. (2011) ‘Soil erosion assessment on tillage and alternative soil managements in a Sicilian vineyard’, Soil and Tillage Research, 117, pp. 140–7. https://doi.org/10.1016/j.still.2011.09.007CrossRefGoogle Scholar
Novara, A., Minacapilli, M., Santoro, A., Rodrigo-Comino, J., Carrubba, A., Sarno, M., Venezia, G. and Gristina, L. (2019) ‘Real cover crops contribution to soil organic carbon sequestration in sloping vineyard’, Science of The Total Environment, 652, pp. 300–6. https://doi.org/10.1016/j.scitotenv.2018.10.247CrossRefGoogle ScholarPubMed
O'Connell, N. and Snyder, R. (1999) ‘Cover crops, mulch lower night temperatures in citrus’, California Agriculture, 53(5), pp. 37–40.CrossRefGoogle Scholar
Oksanen, J., Blanchet, F.G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P.R., O'Hara, R.B., Simpson, G.L., Solymos, P., Stevens, M.H.H., Szoecs, E. and Wagner, H. (2020) vegan: Community Ecology Package. Available at: https://CRAN.R-project.org/package=veganGoogle Scholar
Pearson, D.E., Ortega, Y.K. and Maron, J.L. (2017) ‘The tortoise and the hare: reducing resource availability shifts competitive balance between plant species’, Journal of Ecology, 105(4), pp. 999–1009. https://doi.org/10.1111/1365-2745.12736CrossRefGoogle Scholar
Pierret, A., Maeght, J.-L., Clément, C., Montoroi, J.-P., Hartmann, C. and Gonkhamdee, S. (2016) ‘Understanding deep roots and their functions in ecosystems: an advocacy for more unconventional research’, Annals of Botany, 118(4), pp. 621–35. https://doi.org/10.1093/aob/mcw130CrossRefGoogle ScholarPubMed
Pittman, K.B., Barney, J.N. and Flessner, M.L. (2019) ‘Horseweed (Conyza canadensis) suppression from cover crop mixtures and fall-applied residual herbicides’, Weed Technology, 33(2), pp. 303–11. https://doi.org/10.1017/wet.2018.111CrossRefGoogle Scholar
Putnam, A.R., Defrank, J. and Barnes, J.P. (1983) ‘Exploitation of allelopathy for weed control in annual and perennial cropping systems’, Journal of Chemical Ecology, 9(8), pp. 1001–10. https://doi.org/10.1007/BF00982207CrossRefGoogle ScholarPubMed
R Core Team. (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Available at: https://www.R-project.org/Google Scholar
Ramírez-García, J., Carrillo, J.M., Ruiz, M., Alonso-Ayuso, M. and Quemada, M. (2015) ‘Multicriteria decision analysis applied to cover crop species and cultivars selection’, Field Crops Research, 175, pp. 106–15. https://doi.org/10.1016/j.fcr.2015.02.008CrossRefGoogle Scholar
Ramos, M.E., Benítez, E., García, P.A. and Robles, A.B. (2010) ‘Cover crops under different managements vs. frequent tillage in almond orchards in semiarid conditions: effects on soil quality’, Applied Soil Ecology, 44(1), pp. 6–14. https://doi.org/10.1016/j.apsoil.2009.08.005CrossRefGoogle Scholar
Reiss, E.R. and Drinkwater, L.E. (2018) ‘Cultivar mixtures: a meta-analysis of the effect of intraspecific diversity on crop yield’, Ecological Applications, 28(1), pp. 62–77. https://doi.org/10.1002/eap.1629CrossRefGoogle ScholarPubMed
Renwick, L.L.R., Deen, W., Silva, L., Gilbert, M.E., Maxwell, T., Bowles, T.M. and Gaudin, A.C.M. (2021) ‘Long-term crop rotation diversification enhances maize drought resistance through soil organic matter', Environmental Research Letters, 16(8), p. 084067. https://doi.org/10.1088/1748-9326/ac1468CrossRefGoogle Scholar
Romdhane, S., Spor, A., Busset, H., Falchetto, L., Martin, J., Bizouard, F., Bru, D., Breuil, M.-C., Philippot, L. and Cordeau, S. (2019) ‘Cover crop management practices rather than composition of cover crop mixtures affect bacterial communities in no-till agroecosystems’, Frontiers in Microbiology, 10, Article 1618. https://doi.org/10.3389/fmicb.2019.01618CrossRefGoogle ScholarPubMed
Sánchez, E.E., Giayetto, A., Cichón, L., Fernández, D., Aruani, M.C. and Curetti, M. (2007) ‘Cover crops influence soil properties and tree performance in an organic apple (Malus domestica Borkh) orchard in northern Patagonia’, Plant and Soil, 292(1), pp. 193–203. https://doi.org/10.1007/s11104-007-9215-7CrossRefGoogle Scholar
Schipanski, M.E., Barbercheck, M., Douglas, M.R., Finney, D.M., Haider, K., Kaye, J.P., Kemanian, A.R., Mortensen, D.A., Ryan, M.R., Tooker, J. and White, C. (2014) ‘A framework for evaluating ecosystem services provided by cover crops in agroecosystems’, Agricultural Systems, 125, pp. 12–22. https://doi.org/10.1016/j.agsy.2013.11.004CrossRefGoogle Scholar
Smith, R.G., Atwood, L.W. and Warren, N.D. (2014) ‘Increased productivity of a cover crop mixture is not associated with enhanced agroecosystem services’, PLoS ONE, 9(5), p. e97351. https://doi.org/10.1371/journal.pone.0097351Google Scholar
Smith, R.G. and Gross, K.L. (2007) ‘Assembly of weed communities along a crop diversity gradient’, Journal of Applied Ecology, 44(5), pp. 1046–56. https://doi.org/10.1111/j.1365-2664.2007.01335.xCrossRefGoogle Scholar
Smith, R.G., Mortensen, D.A. and Ryan, M.R. (2010) ‘A new hypothesis for the functional role of diversity in mediating resource pools and weed–crop competition in agroecosystems’, Weed Research, 50(1), pp. 37–48. https://doi.org/10.1111/j.1365-3180.2009.00745.xCrossRefGoogle Scholar
Smith, R.G., Warren, N.D. and Cordeau, S. (2020) ‘Are cover crop mixtures better at suppressing weeds than cover crop monocultures?’, Weed Science, 68(2), pp. 186–94. https://doi.org/10.1017/wsc.2020.12CrossRefGoogle Scholar
Stefan, L., Engbersen, N. and Schöb, C. (2021) ‘Crop–weed relationships are context-dependent and cannot fully explain the positive effects of intercropping on yield’, Ecological Applications, 31(4), p. e02311. https://doi.org/10.1002/eap.2311CrossRefGoogle ScholarPubMed
Tamburini, G., Bommarco, R., Wanger, T.C., Kremen, C., van der Heijden, M.G.A., Liebman, M. and Hallin, S. (2020) ‘Agricultural diversification promotes multiple ecosystem services without compromising yield’, Science Advances, 6(45), p. eaba1715. https://doi.org/10.1126/sciadv.aba1715CrossRefGoogle ScholarPubMed
Teasdale, J.R., Beste, C.E. and Potts, W.E. (1991) ‘Response of weeds to tillage and cover crop residue’, Weed Science, 39(2), pp. 195–9. https://doi.org/10.1017/S0043174500071460CrossRefGoogle Scholar
Tilman, D., Wedin, D. and Knops, J. (1996) ‘Productivity and sustainability influenced by biodiversity in grassland ecosystems’, Nature, 379(6567), pp. 718–20. https://doi.org/10.1038/379718a0CrossRefGoogle Scholar
Trichard, A., Alignier, A., Chauvel, B. and Petit, S. (2013) ‘Identification of weed community traits response to conservation agriculture’, Agriculture, Ecosystems & Environment, 179, pp. 179–86. https://doi.org/10.1016/j.agee.2013.08.012CrossRefGoogle Scholar
Unger, P.W. and Vigil, M.F. (1998) ‘Cover crop effects on soil water relationships’, Journal of Soil and Water Conservation, 53(3), pp. 200–7.Google Scholar
Walsh, B.D., MacKenzie, A.F., Salmins, S. and Buszard, D.J. (1996) ‘Impact of soil management systems on organic dwarf apple orchards and soil aggregate stability, bulk density, temperature and water content’, Canadian Journal of Soil Science, 76(2), pp. 203–9. https://doi.org/10.4141/cjss96-028CrossRefGoogle Scholar
Weiner, J. (1990) ‘Asymmetric competition in plant populations’, Trends in Ecology & Evolution, 5(11), pp. 360–4. https://doi.org/10.1016/0169-5347(90)90095-UCrossRefGoogle ScholarPubMed
Wittwer, R.A., Dorn, B., Jossi, W. and van der Heijden, M.G.A. (2017) ‘Cover crops support ecological intensification of arable cropping systems’, Scientific Reports, 7(1), Article 41911. https://doi.org/10.1038/srep41911CrossRefGoogle ScholarPubMed
Wright, D.H. (1991) ‘Correlations between incidence and abundance are expected by chance’, Journal of Biogeography, 18(4), pp. 463–6. https://doi.org/10.2307/2845487CrossRefGoogle Scholar
Figure 0

Table 1. Description of the three commercial almond orchards used as experimental sites in this study

Figure 1

Table 2. Description and dates of cover crop management actions at each of the three commercial almond orchards over two years in this study

Figure 2

Table 3. Description of the two cover crop and two commercial standard ground cover treatments evaluated in this experiment

Figure 3

Figure 1. Impacts of various four cover crop treatments on amount of uncovered ground soils in orchard alleyways (2018 and 2019). Two cover crop mixes (one consisting of functionally diverse species and the other consisting of uniform species) were compared against two commercially standard orchard management treatments (a treatment accommodated some resident vegetation and a higher intensity treatment to maintain bare ground) in three commercial orchards in Kern, Merced, and Tehama Counties, California, USA (the bare treatment was not included at the Tehama County site). The center line represents median, hinges represent first and third quartiles, and whiskers represent minimum and maximum values within 150% of the interquartile range.

Figure 4

Figure 2. Relationship between cover crop and weed cover in orchard alleyways (2018 and 2019). The line displays marginal replacement of each vegetation type relative to the other as determined by a linear model. Point shapes represent two different cover crop species mixes (one consisting of functionally diverse species and the other consisting of uniform species), though cover crop treatment was not a predictor of weed cover (P = 0.56) and was not included in linear model displayed here.

Figure 5

Figure 3. Stability of cover crop cover in orchard alleyways for two cover crop mixes (one consisting of functionally diverse species and the other consisting of uniform species) (2018 and 2019). Points show the average coefficient of variation across six site-years in this study, and bars show 95% confidence intervals. The diverse cover crop mix exhibited less variation in ground cover compared to the uniform mix (P = 0.035).

Figure 6

Table 4. Weed species found at each site over the course of the experiment

Figure 7

Figure 4. Ordination plots representing weed communities in orchard alleyways (2018 and 2019). Two cover crop mixes (one consisting of functionally diverse species and the other consisting of uniform species) were compared against two commercially standard orchard management treatments (a treatment accommodated some resident vegetation and a higher intensity treatment to maintain bare ground) in three commercial orchards in Kern, Merced, and Tehama Counties, California, USA (the bare treatment was not included at the Tehama County site). Plots were created with nonmetric multidimensional scaling. Each panel was created with from the same ordination analysis but displays points from only one site.