Stability, resilience, and productivity of horticultural agroforestry systems. A meta-analysis

doi:10.21203/rs.3.rs-7459153/v1

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Stability, resilience, and productivity of horticultural agroforestry systems. A meta-analysis

https://doi.org/10.21203/rs.3.rs-7459153/v1

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Diversification of perennial systems with agroforestry can provide a range of agronomic, economic, and environmental benefits along with dietary advantages when combining crops that yield both high-energy and nutritious foods. However, the degree to which agroforestry impacts system resilience and yield stability to climate shocks remains uncertain. We conducted a comprehensive meta-analysis of the published peer reviewed literature focused on agroforestry systems which included a perennial horticultural main crop and an intercrop. We tested the hypothesis that horticultural agroforestry systems can provide (1) higher yield, protein, carbohydrate, and lipid output per unit of land cultivated, (2) greater yield stability, and (3) resilience to abnormal climatic events than monocropping. We show that horticultural agroforestry systems are more productive in terms of yield and carbohydrate output and can confer greater yield stability than when individual crops are grown alone. Land equivalent ratio and net effect ratios increased with agroforestry by 60 and 80%, respectively, despite reduction in main crop yield (approximately 11%). Precipitation emerged as the main factor influencing variability in outcomes, with yield stabilizing effects of agroforestry over time mainly occurring under drought conditions. These findings contribute insight to the ongoing discourse of agroforestry as climate smart practices with implications for both fundamental and applied agricultural research aimed at enhancing resilience and productivity in the face of climate change. This meta-analysis also highlights critical knowledge gaps and the need for further research to fully characterize the climate resilience of these systems.

Modern specialized agricultural systems have been successful in increasing caloric production following the green revolution, but this success has come at a steep cost (Ramankutty et al. 2018; Scheffer et al. 2001). Simplified systems are also increasingly brittle under discrete climate variability, particularly when reliant on high external inputs and exposed to frequent field-level acute and chronic disturbances (Peterson et al. 2018; Petersen-Rockney et al. 2021). To be capable of handling climate shocks, agroecosystems need to be managed for resilience with greater yield and nutritional stability while maintaining productivity (Li et al. 2019; Rist et al. 2014).

Research suggests that diversifying systems temporally or spatially can increase land productivity while also potentially building resilience to extreme weather events. Diversification can offer sources of resilience by reducing single crop vulnerability while enhancing robustness to environmental stressors (Peterson et al. 2020) though with varying and often negative tradeoffs for productivity (Li et al. 2019; Pittelkow et al. 2015). Productivity in a diverse cropping system is largely driven by complementarity in developmental and physiological processes between species (Fukai and Trenbath 1993) as well as site growing conditions (Bybee-Finley et al. 2024). Diversifying annual crop rotations has been shown to reduce crop failure and mitigate against climate shocks, improve yield stability, and resource-use efficiency (Gaudin et al. 2015; Reiss and Drinkwater 2018). Increasing the number of crops grown in rotation with maize has also been shown to improve long-term yield resilience (Bowles et al. 2020), particularly in the face of drought (Sanford et al. 2021; Renwick et al. 2020). Intercropping designs can also increase long-term yield and income for smallholder farms (Chai et al. 2021), providing additional dietary benefits when growing high energy nutritious foods (Li et al. 2023). There have been promising results that intercropping can also improve the nutritional quality of a crop mix, particularly for forage crops (Toker et al. 2024).

While most of the existing research on crop diversification has focused on annual and grain cropping systems, there remains a significant gap in understanding resilience potential of more diversified perennial horticultural systems that are highly nutritionally relevant. These horticultural agroforestry systems have potential for reducing climate related risk while maintaining productivity due to complementarity mechanisms (i.e. shading, rooting depth, and weed control) from combining a perennial main crop with an intercrop (Chemura et al. 2021). Outcomes are highly species dependent (Scordia et al. 2023), as certain mixtures may interact differently leading to potential synergies (e.g. nutrient facilitation) and/or competition (e.g. niche overlap). These relationships can also change following extreme weather events as negative conditions may promote competition or be partially mitigated by diversity depending on the tree species (Rivest et al. 2013). Results to date have been highly variable and the long-term potential for resilience building remain unclear (Castle et al. 2021).

The goal of this study was to assess the yield and nutrient stability and productivity outcomes of horticultural agroforestry systems. We hypothesized that horticultural agroforestry systems exhibit (1) higher productivity in terms of yield, protein, carbohydrate, and lipid output, (2) more temporally stable yield, and (3) greater resilience following an abnormal climatic event than when sole cropped. To test these hypothesis, we applied a meta-analysis procedure on relevant articles from a dataset by Paut et al (2024) and expanded the search to include more recent articles past their search date (2022–2024). We considered productivity and stability of both individual crop components and of the diversified agroforestry system. This comprehensive analysis of published data from 56 site-years offers early insights into how agroforestry systems can provide a path forward to enhance agricultural stability across diverse contexts, especially amid significant environmental changes, guiding future research and policy decisions.

2.1 Data collection

We used (1) relevant articles from an existing dataset (1995–2022) on intercropping horticultural crops by Paut et al. and (2) searched the Web of Science Core Collection (apps.webofknowledge.com) to extend the dataset (2022–2024). We systematically searched for papers published between 2022–2024 in May 2024 using the search criteria: ((intercrop* OR inter crop* OR agroforest* OR agro-forest* OR “agr*s*lv*cult*” OR agrihortisilvicult* OR “woody polycultur*” OR “mixed crop*” OR “alley crop*” OR “home garden*” OR “forest garden*” OR “multilayer tree garden*” OR “fruit-vegetable crop*”) AND (fruit* OR orchard* OR vegetable* OR legume* OR “market garden*” OR horticultur*) AND (LER OR “land equivalent ratio” OR yield* OR “agronomic performanc*” OR productivity OR profitability)).

Articles were uploaded and manually screened using the AI Rayyan (new.rayyan.ai). The abstract needed to (1) report horticultural crop grown in agroforestry and as sole crops, and (2) only comparing two crops, (3) report annual yield data, (4) be published in a peer-reviewed journal, (5) written in English and (6) available in open access or through author’s institutional access. The full-text article needed to report (7) raw data, (8) total yield for crops as intercrops and as sole crops with data available for every year, (9) location and year of experiment, and (10) span a minimum of 2 years in 2 locations, or 3 years in one location. We did not include data from modeling studies or other meta-analyses. The flow-chart with the PRISMA selection criteria is shown in Supplementary Fig. 1. This resulted in a dataset of 13 articles encompassing three agroforestry designs with a horticultural main crop, intercrop, or both (Fig. 1) where the perennial main crop and the intercrop were grown in comparison to their component monocultures. Raw data was extracted directly from tables, text, and graphs using WebPlotDigitizer (v4; Ankit Rohatgi, 2024). A summary of moderating variables collected within the database are available in Supplementary Table 1. A list of the 33 unique intercropping combinations and associated mixture proportions is provided in Supplementary Table 2.

2.2 System productivity metrics

System productivity was quantified for yield and nutrients using four different ratios (Table 1): (1) Land Equivalent Ratio (LER), (2) partial Land Equivalent Ratio (pLER), (3) Net Effect Ratio (NER), and (4) Transgressive Overyielding Index (TOI). These metrics were used to assess agroforestry performance over sole cropping and normalized across the selected studies (Li et al. 2023). LER measures land use efficiency by comparing the relative productivity of component crops grown under agroforestry to monocropping assuming a 50:50 cropping mixture. NER represents the additional yield obtained per unit area in real terms by accounting for the proportion of component crops in the agroforestry mixture compared to expected productivity based on sole cropping yields. Crop density within the mixture was determined using the percentage occupied by each crop per hectare (equaling 100% total) (Table S2). Lastly, TOI shows the yield advantage for agroforestry compared to the highest yielding sole crop, recognizing that farm production goals for maximizing output per unit area may favor monocropping of one highly productive crop rather than splitting area into two component crops.

Table 1
The four ratios used to evaluate system performance in agroforestry compared to monocropping. The yield of the main crop (MC) and intercrop (IC) are compared in the agroforestry treatment (T) to the sole cropping control (C) as a function of crop density proportions (P) within the crop mixtures. Within these metrics, values > 1 indicate increased productivity in agroforestry and < 1 indicate reduced productivity in agroforestry.
Metric	Land Equivalent Ratio (LER) and partial Land Equivalent Ratio (pLER)		Net Effect Ratio (NER)	Transgressive Overyielding Index (TOI)
Formula	LER = pLER_MC + pLER_IC \(\:pLER=\frac{{\stackrel{-}{X}}_{T}}{{\stackrel{-}{X}}_{C}}\)		\(\:NER=\frac{({T}_{MC}+{T}_{IC})}{({P}_{MC}{C}_{MC}\:+{P}_{IC}{C}_{IC})}\)	\(\:TOI=\frac{({T}_{MC}+{T}_{IC})}{max({C}_{MC},{C}_{IC})}\)
Definition		Indicator of land use efficiency. The amount of land required under sole cropping to match the yields of agroforestry, assuming a 50:50 cropping mixture. Values greater than one indicate that agroforestry is more land-efficient than sole cropping.	Indicator of real gains in system productivity per unit area compared to the expected agroforestry yield. Expected yield was calculated by weighting sole crop yields according to the crop mixture proportions in the agroforestry system. Values greater than one indicate that agroforestry yields exceed the expected combined yields of sole crops.	Indicator of total yield gains within agroforestry compared to the most productive sole crop species. TOI compares the maximum productivity across systems and values greater than one indicate that agroforestry over-yielded the highest yielding sole crop.

We calculated the macronutrients (i.e. proteins, calories, and carbohydrates) for each crop using a subset of the dataset based on availability of data from the USDA FoodData Central (fdc.nal.usda.gov) database. Nutrient productivity was evaluated using TOI, as it is the most conservative measure of output nutrients. The nutrient to yield equivalent was calculated as follows:

Nutrient (T/ha) = \(\:\frac{\:Yield\:(T/ha)\:\times\:\:Amount\:Nutrient\:(g/100\:g)\:}{100}\:\)

2.3 Individual crop productivity and stability metrics

Mean response ratios of standard deviation (\(\:\sigma\:)\), coefficient of variation (CV), and yield outputs were used to compare the stability and resilience of agroforestry against monocropping for each individual crop (Table 2). Stability was evaluated using two different metrics: (1) absolute stability, comparing \(\:\sigma\:\) to measure temporal yield variability without considering crop productivity (Knapp and van der Heijden 2018) and (2) relative stability, comparing CV to measure temporal variability scaled per unit yield produced (Nakagawa et al. 2015). Formulas in Table 2 represent the individual performance of the MC and IC to understand whether growing each species under agroforestry contributes to greater stability and resilience. These calculations were repeated for the nutrient subset of the data to evaluate the stability and productivity of total nutrient output.

Table 2
Response ratio metrics to assess the stability and mean yield for the main crop and the intercrop independently, grown in the Agroforestry Treatment (T) and Sole Cropping Control (C). Within these metrics, values > 1 indicate agroforestry had a positive effect and < 1 indicate that agroforestry had a negative effect.
Metric	Mean pLER	Absolute Stability	Relative Stability
Formula	\(\:{R}_{y}\:=\frac{{\stackrel{-}{X}}_{T}}{{\stackrel{-}{X}}_{C}}\)	\(\:{R}_{AS}\:=\frac{{\sigma\:}_{C}}{{\sigma\:}_{T}}\)	\(\:{R}_{RS}\:=\frac{{CV}_{C}}{{CV}_{T}}\) \(\:CV\:=\:\frac{\sigma\:}{\stackrel{-}{X}}\)
Definition	Relative difference in crop yield between the treatment and control across the study, does not account for crop mixture proportion (assumed 50:50).	Relative difference in standard deviation (\(\:\sigma\:\)) of mean crop yields over time between the control and treatment. The ratio is flipped, as a higher \(\:\sigma\:\) indicates lower stability.	Normalized difference in standard deviation scaled by mean crop yield for each treatment. The ratio is flipped, as a higher CV indicates lower stability.

2.4 Effect sizes

Estimated effect size for the productivity and stability metrics were calculated with an unweighted linear mixed-effects model using the lme function in the R package nmle (Pinheiro & Bates 2025). To estimate effect sizes of the productivity metrics, we used article and agroforestry combination within the article as random effects to account for differences across studies and observations (Li et al. 2023).

To estimate effect sizes of the stability metrics, we used article and experiment duration as random effects to account for differences across publications and confounding effects from higher data points. The stability linear models were then resampled using an unweighted bootstrapping (1000 iterations) with the boot package in R (Cantey & Ripley 2024) and considered significant if the results did not cross one. All statistical analyses were performed using R statistical software (v4.4.1; R Core Team 2024). Large study heterogeneity was observed (Q = 1107, I² = 92.9) in the dataset. To evaluate potential publication bias, we generated Funnel Plots with the mean pLER using the metafor package (Viechtbauer 2010) and statistically evaluated them using Egger’s regression analysis. Within our dataset, we did not find significant evidence for publication bias (Egger’s p > 0.05, Fig S2).

2.5 Climate calculation and resilience assumptions

Monthly and annual climate data were obtained from the NASA Power Project Data Access Viewer (power.larc.nasa.gov) at the latitude & longitude coordinates reported. This database provides 1980-current climate data with a resolution of 10 km. The 20-year average climate data was calculated based on the 20 years preceding the start date of each study, except for one study that extended beyond the range of available historical data. For that study, the climate data was adjusted using a 10-year historical average based on the available data. Climate deviation (%) from the 20-year average for each study was calculated as follows:

Climate Deviation % =\(\:\:\left(\:\frac{\:Annual\:Mean}{20\:year\:Average}\:\right)\:\times\:\:100\)

To quantify the relative importance of the climate related variables (mean annual temperature, mean annual precipitation, mean minimum temperature in the coldest month, and mean maximum temperature in the warmest month) we used a random forest permutation model from the MissRanger package and randomForest package (Mayer 2024; Liaw & Wiener 2002). The moderating variables (Table S1) were added to the model and the output evaluated using R². Resilience was then quantified using Kendall’s rank correlation test to assess the stabilizing effect of agroforestry following high climate deviation of the most important moderator.

3.1 Database and scope of the meta-analysis

The final dataset contained 13 articles (Figure S1) with 250 observations at 14 locations across 6 climate zones and a total of 56 site years (Fig. 2). The database contained 12 unique perennial main crops and 28 unique intercrops (Table S2), where the intercrops were a mix of both perennial (n = 138) and annual (n = 112) crops.

The 13 articles included in our dataset covered five continents with 53% the observations originating from East and South Asia. Due to our strict inclusion criteria which prioritized long-term studies, data from certain regions, such as Northern and Central America and Australia and New Zealand, were notably underrepresented. Given the low number of studies and high variability in crops and ecological contexts, these results should be considered a limited baseline for future work. Nonetheless, our dataset spans a broad range of representative latitudes, from − 22.4° to 43.6°. The most frequently observed main crop families were Solanaceae (23%), Rosaceae (22%), and Rubiaceae (19%), while Poaceae (34%) and Fabaceae (22%) were the most common intercrop families.

3.2 Effect of agroforestry on productivity

Agroforestry increased land use efficiency by 60% (mean: 1.6 [95% CI: 1.44, 1.75]), indicating higher combined yields under agroforestry compared to sole cropping (Fig. 3). When accounting for the mixture proportion in each study, agroforestry increased system yields by 80% (mean: 1.8 [95% CI: 1.59, 1.99]), suggesting that species with lower density had even higher pLER. The average proportion of the main crop and intercrop in agroforestry was 58% and 42% respectively, with studies ranging from an even mixture (50:50) to a single species dominated mixture (90:10) (Table S2). Horticultural agroforestry systems were more productive than the top-performing sole crop, as indicated bt TOI being greater than one. Overall, agroforestry over-yielded by 20% (mean: 1.2 [95% CI: 1.06, 1.34]) the most productive sole cropping species. Within the macronutrients, only the carbohydrate output was different from one, showing a benefit (mean: 1.6 [95% CI: 1.44, 1.75]).

System productivity varied by crop and crop family and the best performing crop family in agroforestry was Anacardiaceae (Fig. 4, Fig S3). Agroforestry resulted in the mean pLER varying between the main crop and intercrop (Fig. 5; 0.92 and 0.76 for the MC and I respectively) with the main crop (estimated mean 0.89 [95% CI: 0.84, 0.93]) being more productive than the intercrop (estimated mean 0.80 [95% CI: 0.76, 0.85]). This result indicates that while each crop component experienced a yield reduction within agroforestry (Fig. 5), their combined output resulted in a net increase in yield (Fig. 3).

3.3 Stability of individual crop yield and nutrient output

Yield stability of the main crop and intercrop differed (Welch t-test: p < 0.05) (Fig. 5). Relative stability increased for the main crop (estimated mean 1.12 [95% CI: 1.00, 1.24]) but not for the intercrop (estimated mean 0.98 [95% CI: 0.98, 1.16]), while absolute stability significantly increased for both the main crop (estimated mean 1.34 [95% CI: 1.22, 1.47]) and intercrop (estimated mean 1.45 [95% CI: 1.15, 1.83]). Macronutrient stability and mean pLER followed similar trends (Fig S4). Lifecycle of the intercrop (perennial or annual) did not significantly affect the mean pLER (p = 0.11), absolute stability (p = 0.76), or relative stability (p = 0.34).

3.4 Impact of annual precipitation and deviations from normal on stability

Drivers of individual crop absolute stability differed between the main crop and the intercrop (Fig. 6). The most important climate factor regulating the main crop absolute stability was the historical mean maximum temperature of the warmest month, mean annual precipitation, and historical precipitation levels. For the intercrop, the most important moderators were annual precipitation, historical precipitation, mean minimum temperature of the coldest month, and the mean annual temperature. The most important climate moderators for relative stability were mean annual temperature and mean annual precipitation for the main crop and historical maximum temperature of the warmest month and mean maximum of the warmest month (Fig S5).

Resilience following a climate shock (i.e. high climate deviation) within the dataset was unclear, as no significant trend was found despite trends between stability metrics and deviation in precipitation (MC: p = 0.64; IC: p = 0.09) (Fig S6), annual temperature (MC: p = 0.66; IC: p = 0.48), minimum temperature (MC: p = 0.66; IC: p = 0.48), and maximum temperature (MC: p = 0.12; IC: p = 0.14).

We conducted a meta-analysis of published data to measure outcomes of horticultural agroforestry systems on (1) yield and nutrients productivity, (2) temporal yield stability, and (3) resilience to abnormal climatic event compared to sole cropped. We found that agroforestry systems offer greater long-term stability and total yield and carbohydrates, highlighting the potential to boost land productivity by combining horticultural species as a MC and/or IC, offsetting lower individual crop productivity.

4.2 Agroforestry increases system productivity

Without accounting for mixture proportion, combining two crops in agroforestry reduced yield of individual crops compared to sole cropping (Fig. 5). This individual component reduction follows the results observed in other crops. Scordia et al. (2023) found a 26% reduction in crop yield when trees were present, Ivezic et al. (2021) found a 4% reduction in yield for cereal crops in agroforestry, and Li et al. (2023) found that intercropping reduced maximum grain yield by 4%. This apparent variability in crop performance is driven by competition for resources in crop species interactions (Li et al. 2019). Accordingly, intercropping has been found to be most productive when each crop is temporally asynchronous at the time of their maximum resource demand (Fukai and Trenbath 1993). Thus, productivity is highly species driven (Fig S3) and context specific (Zhu et al. 2023). Overall, these findings suggest that careful consideration and optimization are necessary when implementing an agroforestry system to improve productivity.

Agroforestry demonstrated higher system productivity compared to monoculture both in terms of land use efficiency (LER) and yield per-unit-area in real terms (NER) (Fig. 3). Total yield per unit area in agroforestry systems increased by up to 80% and exceeded the yield of the highest yielding sole cropped component by 20%, with a corresponding 60% increase in land use efficiency. These values are much higher than recently reported in a global synthesis of grain production, where intercropping enhanced LER and NER by 19 and 28%, respectively. Notably, the nutrient pLER was higher than the yield pLER for both the main crop (estimated mean 0.91 [95% CI: 0.85, 0.98]) and the intercrop (estimated mean 0.83 [95% CI: 0.79, 0.88]) (Fig S4). Only carbohydrates over-yielded in agroforestry systems, while no significant differences were observed in lipid or protein outputs. The system with the highest carbohydrate output compared to sole cropping was pigeon pea intercropped with maize (TOI Carb.: 1.9) (Rusinamhodzi et al. 2017). This gain in carbohydrate highlights the potential of integrating grains into perennial systems for long-term productivity. Overyielding in intercropping systems has been found to be weakened by high inputs (Zhu et al. 2023) and implementation may be optimized in low-input systems or sites with poor growing conditions (Bybee-Finley et al. 2024).

Careful consideration for species selection and adaptation of these practices is necessary to optimize both individual crops and system productivity. For example, the perennial main crop species in our meta-analysis appeared to be well suited for intercropping (e.g. olive, almond, goji berry, apple), as the mean pLER was 0.89 despite only representing around 58% of the mixture proportion. Contrastingly, the intercrop species did not perform as well, as Poaceae and Fabaceae, 56% of the intercrops’ observations, were the worst performing crop families within our dataset (Fig. 4). We found that system performance was significantly influenced by the species of intercrop (p < 0.001), though lack of data did not allow us to fully evaluate the effect of species mix on productivity. Managing towards maximizing long-term productivity takes careful consideration of niche complementarities towards trait-based facilitation and resource sharing (Brooker et al. 2015). A clear example of this management within our dataset is a coffee-based agroforestry system (Tehulie and Nigatie 2023), where canopy shading from banana leaves slows coffee fruit maturation and resource competition drives productivity (van Asten et al. 2011).

4.3 Crop stability and resilience increased in Agroforestry

Previous meta-analyses in annual systems have shown a positive correlation between crop diversity and temporal stability in yields and in-field resilience (Stomph et al. 2020). To the best of our knowledge, this is the first study that demonstrates the improved stability of horticultural crops grown in an agroforestry system, highlight the potential of diversified perennial systems for long-term crop resilience and farmer risk-mitigation. Stability outcomes were influenced by the intercrop present and certain crop mixes lead to instability and productivity loss such as when goji berry was intercropped with sweet sorghum (Zhu et al. 2023). These two crops overlap in their seasonal development and maximum resource demand, particularly during fruit/grain onset, and performed significantly better when sole cropped. Resilience likely results from overlapping resource demand and contrasting competitive aggressivity (Herrick and Blesh 2021; Willey and Rao 1980). Functional-trait diversity can facilitate nutrient cycling through nitrogen fixation, phosphorous and micronutrient acquisition (Nesper et al. 2019), and nutrient facilitation during times of drought (Rivest et al. 2013). These polycultures contribute to a robust agroecosystem in the face of climate change and biodiversity loss; through increasing soil organic carbon (Beillouin et al. 2023), pollination (Blüthgen and Klein 2011), and reducing pest and disease pressure (Yousefi et al. 2024).

Drought conditions were prevalent across studies (64% of the 250 observations showed a decrease in annual precipitation) suggesting that horticultural agroforestry systems have a stabilizing effect on yield in drought conditions. The included perennial mixtures may have been more stable against drought stress through several mechanisms, such as reducing soil evaporation via shading, increasing soil water retention, and crop rooting characteristics. Since many of the sites within our study were irrigated (53%), we were unable to fully isolate precipitation stress and yield resistance to drought. Chronic climate effects in our study also likely had a stronger long-term impact on the main crop than we were able to measure. This shows that incorporating horticultural agroforestry systems could be a key strategy for mitigating climate-related risks, enhancing resilience to both extreme weather events and long-term environmental changes.

Our analysis highlights the potential that horticultural agroforestry has for increasing biodiversity to sustainably intensify agriculture. We found that agroforestry increases both system productivity and crop stability with an expected trade-off in individual crop yield. Agroforestry systems demonstrated a significant enhancement in carbohydrate nutrient output relative to sole cropping, whereas protein and lipid output remained unchanged. The most important moderator for stability was precipitation, highlighting trends of crops performing better in agroforestry under extreme variability in precipitation. Our analysis is limited by the number of long-term studies conducted, so we were unable to fully model this interaction. To fully elucidate the impact of climate change on agroforestry systems and their resilience building potential, more long-term research needs to be conducted on horticultural agroforestry systems across contexts and using integrated assessment of systems outcomes.

Funding

Funding for this work was provided by the Department of Plant Sciences at the University of California through a Graduate Student Research Award and Jastro-Shields Research Award to DM and the Endowed Professorship in Agroecology fund to AG.

Conflicts of Interest

The authors declare no competing interests.

Ethics Approval

Not applicable

Consent to Participate

Not applicable

Consent for publication

By submitting this manuscript for publication, we, the authors, provide our consent for its publication in the designated journal. We affirm that this manuscript is original, has not been previously published, and is not under consideration for publication elsewhere. We take responsibility for the content and integrity of the manuscript and declare that all the listed authors have made substantial contributions to the study.

Authors Contributions

DM conceptualized and designed the study, performed the literature review, data screening and extraction, meta-analysis, and prepared the first draft of the manuscript. CMP interpreted the results and contributed to the revision. AG discussed the study, guided writing and data analysis and edited the manuscript

Data Availability Statement

The database presented in this study can be found in online repositories at:

[https://doi.org/10.5061/dryad.xgxd254t7]

Code Availability

All statistical code was generated in R statistical software (v4.4.1; R Core Team 2024) and is available upon reasonable request.

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Stability, resilience, and productivity of horticultural agroforestry systems. A meta-analysis

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Abstract

Figures

1 Introduction

2 Methods

2.1 Data collection

2.2 System productivity metrics

2.3 Individual crop productivity and stability metrics

2.4 Effect sizes

2.5 Climate calculation and resilience assumptions

3 Results

3.1 Database and scope of the meta-analysis

3.2 Effect of agroforestry on productivity

3.3 Stability of individual crop yield and nutrient output

3.4 Impact of annual precipitation and deviations from normal on stability

4 Discussion

4.2 Agroforestry increases system productivity

4.3 Crop stability and resilience increased in Agroforestry

5 Conclusion

Declarations

Funding

Conflicts of Interest

Ethics Approval

Consent to Participate

Consent for publication

Authors Contributions

Data Availability Statement

Code Availability

References

Supplementary Files

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