Long-term agricultural diversification increases financial profitability, biodiversity, and ecosystem services: a second-order meta-analysis

Domain: neuroscience
Published: 2026-01-26
Editorial outcome: major_revision

Abstract
Sustainable agriculture in the 21st century requires the production of sufficient food, while reducing environmental impacts and safeguarding human livelihoods. Many studies have confirmed agricultural diversification with practices such as intercropping, organic farming, and soil inoculations, as suitable pathways to achieve these goals, but the long-term viability of socioeconomic and ecological benefits is uncertain. Here, we use a second-order meta-analysis to quantify long-term effects of agricultural diversification practices on socioeconomic and ecological benefits based on 120 years of data from 184 meta-analyses with 6741 effect sizes. Diversification increases financial profitability, biodiversity, pollination, soil quality, and carbon sequestration from 37 to 189% over 20 years, and we find no significant effects on crop yield, pest control, and climate regulation. Non-crop and crop diversification practices and use of organic amendments increase or maintain most of these benefits over time. Trade-off analysis between yield and all services shows win-win outcomes during the first 40 years. Our synthesis provides the urgently needed evidence for farmers and other decision-makers that diversification increases long-term financial profitability, biodiversity, soil quality, and climate change mitigation benefits. A large-scale implementation of agricultural diversification requires, however, careful consideration of factors that mediate these benefits. Then, agricultural diversification can be upscaled for long-term socioeconomic and nature-positive outcomes and, ultimately, for a global food system transformation.

Introduction
Transforming the global food system requires nature-positive production systems1,2 using agroecological farming methods based on agricultural diversification practices to achieve sustainable food production3–5. Agricultural diversification is based on farming practices, such as crop rotation, agroforestry, or organic farming, that purposely integrate functional diversity into production systems to enhance or maintain the services essential to crop production6. Several syntheses have confirmed the socioeconomic (i.e., yield and profitability) and environmental (i.e., biological communities, soil quality, and climate mitigation) benefits of agricultural diversification practices for crop production worldwide7–9. In Asia, for example, rice crop diversification practices reduce pesticide use and increase biocontrol and crop yields10. These benefits have incentivized stakeholder groups to implement agricultural diversification across different countries. In China, agricultural diversification was recently promoted into the country’s major agricultural policies11. In Europe and the USA, several agricultural diversification practices, such as organic farming and crop/non-crop diversification, are advocated in agricultural policies for biodiversity conservation and climate change mitigation12–14. Despite these successes, the long-term effects of agricultural diversification on socioeconomic and environmental factors remain unquantified and impede large-scale implementation by risk-averse farmers and promotion through policymakers.

Stakeholder groups require long-term evidence that diversified agricultural production can maintain and ideally improve livelihoods, economy, and the environment to mitigate climate change and external stressors15–17. For instance, in staple foods like rice, the socioeconomic and environmental benefits of diversified rice farming are now well quantified18, but implementation success will depend on understanding temporal dynamics of such benefits15. In cash crops such as cocoa, smallholder farmers have made long-term investments in their tree plantations and may be reluctant to transition towards diversified agroforestry systems without understanding the long-term effects on profitability and yields19. More generally, global food security requires policy decisions that assure reaching the Sustainable Development Goals now more than ever20–22. Moving towards an understanding and quantification of the long-term effects of agricultural diversification practices on socioeconomic and environmental factors-and the trade-offs between them is, hence, essential to the successful implementation of a global food system transformation20,23,24.

Here, we conduct a global synthesis of meta-analyses to quantify the effects of agricultural diversification practices on socioeconomic and environmental factors for up to 120 years. Based on data from 184 meta-analyses with 6741 effect sizes based on 17,989 original studies (Supplementary Fig. 1), we quantify the influence of duration (years) of diversification practices on their effects on socioeconomic factors (crop yield and financial profitability), biological communities (biodiversity, pollination, and pest control), soil quality (water regulation, soil fertility, and nutrient cycling), and climate change mitigation (carbon sequestration and climate regulation). We also analyse the temporal trade-offs between crop yield and ecosystem services (Methods).

Results
Socioeconomic, biological community, soil quality, and climate change mitigation benefits mostly increase over time

Our second-order meta-regression models showed that agricultural diversification practices significantly increased benefits for six out of ten socioeconomic and environmental factors in the long term (Fig. 1, Supplementary Table 1). The analysis of socioeconomic factors showed that while agricultural diversification did not significantly change crop yield overtime (Fig. 1), financial profitability increased significantly by 189% over 20 years (LnRR = 0.3633) compared to short-term effects (<3 years, LnRR = 0.1259, Supplementary Fig. 2, Supplementary Table 2). Our analyses also showed that, over time, the probability of agricultural diversification having no effect on crop yield increased (Supplementary Fig. 1). These findings show that, while farmers may not experience significant benefits in terms of crop yield from diversified farming systems over time, their income improves, highlighting the socioeconomic viability of agricultural diversification in the long run3.

For biological communities, agricultural diversification significantly increased biodiversity by 64% over a 20-year period and pollination benefits by 629% over 10 years (Fig. 1, Supplementary Table 2). These patterns provide evidence that agricultural diversification can play an important role not only in mitigating biodiversity losses from agriculture25 but also in improving pollination services26, which is highly important for global food security27.

The mean effect sizes of soil quality and climate change mitigation, except for climate regulation, were significantly positive. Soil fertility, nutrient cycling and carbon sequestration had significant increases over time (Fig. 1), with benefits of 37% to 107% over a 20-year period (Supplementary Table 2). These patterns suggest that, in the long run, the benefits of agricultural diversification for soil fertility, nutrient cycling, and carbon sequestration are increasing, while the benefits of water regulation are maintained. The transformation from simple monocultures to diversified farming systems, therefore, can improve soil quality and carbon stocks in agricultural lands sustainably, with greater benefits in the long term28,29.

Overall, our results provide evidence that agricultural diversification can help to achieve more sustainable and climate change resilient food production systems1,30 with financial profitability, biodiversity, pollination, soil fertility, nutrient cycling, and carbon sequestration being 22% to 110% higher after 20 years compared to non-temporal analysis results (Supplementary Fig. 2, Supplementary Table 2). Our analyses show that non-temporal analyses vastly underestimate the full potential that agricultural diversification can bring for socioeconomic and environmental factors after just two decades20,30. These aspects support further the importance of considering the duration of agricultural diversification practices when evaluating their effects on the natural environment28,31,32.

Our meta-regression analyses on the effects of six diversification practices on socioeconomic factors, biological community, soil quality, and climate change categories showed that almost half of the response variables became significantly positive or increased over time (green in Fig. 2, Supplementary Tables 3, 4). Non-crop diversification and organic amendments increased response variables in all four categories over time (Fig. 2, Supplementary Table 4), which provides realistic opportunities to address the twin challenges of biodiversity loss25,33 and climate mitigation34,35. In addition, the long-term maintenance or significant improvement of soil fertility and nutrient cycling shows enhancement of soil quality34,36,37. Long-term benefits of organic amendments highlight the potential of this practice to reduce chemical inputs, and hence, pollution and health repercussions38,39. Therefore, these practices constitute the best options for shifting away from unsustainable and towards more biodiversity and nature-friendly farming practices3,20. It is noteworthy, however, that certain practices, such as embedded natural habitats or flower strips require more land and may limit the yield per unit area. Additionally, the effectiveness of the uses of organic amendments depends on factors such as the application rate of organic inputs40,41, as well as the availability of these inputs42. In some cases, the optimal quantity of organic materials required by certain farmlands for maximum benefits43 might exceed the land’s capacity to produce such material. In such cases, the use of external organic fertilizers may be necessary and could hinder large-scale implementation of these practices. Therefore, reaping the long-term benefits of these practices requires a nuanced consideration of implementation conditions and limitations.

Among all the types of agricultural diversification practices, crop diversification was the only practice that significantly increased financial profitability and resulted in non-significant effects or changes on yields over time (Fig. 2, Supplementary Table 4). Crop diversification can, hence, improve farmers’ income over time44 and ensure the long-term maintenance of crop yield45. It is worth noting that, although crop diversification practices do not necessarily increase crop yield compared to conventional practices, they can contribute to food security through diet diversification and might help improve resistance to external shocks and climate change adaptation46–48. These benefits may be limited by non-productive years in case of temporal crop diversification and by land availability for spatial crop diversification48. Our result also showed that crop diversification effects on pest control services did not change significantly over time, and there was no significant difference compared to conventional agriculture for up to 50 years (Fig. 2, Supplementary Table 4). This result indicates that some known benefits for instance of crop rotation on pest control may be counteracted49, where environmental complexity increases incidence of some pests49–51.

Reduced tillage showed slight yield increases over time but significant long-term benefits for carbon sequestration, while organic farming did not provide significant long-term benefits, except for biodiversity (Fig. 2, Supplementary Table 4). Overall, mainstreaming agricultural diversification and the combination of various practices may be a very effective and long-term strategy for global food system transformation5,9. For instance, non-crop diversification strategies seem to hold most benefits as an individual practice, while organic farming shows an incremental increase in biodiversity benefits over time but has consistently lower yields (but see ref. 52). An effective combination of practices may help to transform some of the non-significant effects into positive long-term benefits8,9.

Long term trade-offs between crop yield and other ecosystem services over time

We predicted trade-offs between crop yield and other services over time based on hierarchical regression models, in which all effect sizes were divided into loss and win outcomes (Methods). Overall, we detected a win-win situation between crop yield and all others services with a probability higher than 75% within the first 50 years of agricultural diversification practices, and then a lose yield-win ecosystem services was the most likely outcome (Fig. 3A; Supplementary Table 5). When examining the trade-off between yield and individual services, we found that increasing win-win situations with biodiversity, water regulation, and climate regulation were predominant (Fig. 3C, F and J, Supplementary Table 5). These results show that the positive and desirable outcomes of agricultural diversification practices not only remain but also become more probable over time2.

Our analyses also showed win-win situations with financial profitability, soil fertility, nutrient cycling, and carbon sequestration for 7, 45, 47, and 25 years, respectively (Fig. 3), and lose yield-win ecosystem service situations becoming more common afterwards, interestingly including lose yield-win financial profitability. Our results indicate that compared to monocultures and conventional agriculture, agricultural diversification can improve crop yield in short terms while increasing farmers’ income, improving soil nutrients, and capturing and storing carbon from the atmosphere in the long run, thus, achieving nature-positive systems and equitable livelihoods for the farmers1,53,54. Government agri-environmental schemes should, hence, compensate for lower yields in diversified systems55. It is important to note, however, that this is highly aggregated information and that these relationships will vary depending on location, crop type, and diversification practice.

Our results showed a predominance of lose yield-win pest control and pollination relationships over the years (up to 15 years for pest control, Fig. 3D, E), indicating that increases in pollination and pest control services do not always translate into higher crop yields. It is noteworthy, however, that while there is not necessarily a yield increase with pollination, 70% of all crops globally depend on natural pollination; hence, pollination is critical for yield stability27,56. In addition, some studies have shown that pollination and pest control services can increase yield quality, thus improving the market value of crop yield57.

Our analysis also revealed a temporal increase of the lose-lose relationship between crop yield and pest control services (Fig. 3E). However, the effects of diversification on pest control services were not statistically significant over time (Fig. 1, Supplementary Tables 1, 2), and the probability of this outcome increased over the years (Supplementary Fig. 1). This indicates that diversified farming may provide concomitant benefits to some pest control agents and pests58, which leads to some negative effects on crop yield. Use of agricultural technologies could help to reduce such undesirable effects of agricultural diversification via the use of pest-resistant varieties or more effective pest control technologies59.

We also found an increase in lose-win relationship between crop yield and carbon sequestration over time (Fig. 3I). This indicates that with increasing effort for carbon capture, farmers face yield decrease, probably due to constraints associated with diversification practices such as smaller areas to plant crops in the case of non-crop diversification9. Thus, climate financing for diversified agriculture ought to consider such carbon-yield trade-offs and require even more advocacy for diversification and agroecology at climate summit discussions.

Methods
Literature search and data compilation

We systematically searched for peer-reviewed meta-analysis papers analysing the effects of agricultural diversification on socioeconomic, biological community, soil quality, and climate change using Web of Science (https://www.webofscience.com/) and Scopus (https://www.scopus.com/). For this, we followed the PRISMA review protocol (Supplementary Fig. 7). More specifically, we searched for all papers published until December 31, 2022, using keywords related to agricultural diversification practices and socioeconomic and ecological factors related to farming practices, and set meta-analysis as the type of study (for search strings, see Supplementary Table 6). Our research yielded 5008 papers, which we then screened using Rayyan (https://www.rayyan.ai/) to determine which papers met the criteria for full-text screening. We considered a paper relevant for full-text screening if it was about agricultural production systems and potentially diversified farming systems. In this study, we defined agricultural diversification or diversified farming systems as any practices that purposely incorporate functional diversity into farming systems6. During screening, we also focused on crop yield, financial profitability, biodiversity, pollination, pest control, water regulation, soil fertility, nutrient cycling, carbon sequestration, and climate regulation, and if it was not a primary study. These criteria resulted in 3692 papers being excluded, leaving 1316 papers for full-text screening (Supplementary Fig. 7).

We screened the full text of 1316 papers individually and found 11 second-order meta-analyses papers related to the topic of this study that focused on at least one of our response variables (see Supplementary Table 7). We also screened the papers from those meta-analyses and ensured that all studies included in these studies were also included in the remaining papers for abstract screening. In each paper, we searched for the following information as inclusion criteria: (1) the study should be a meta-analysis with at least one effect size comparing diversified farming systems to non-diversified practices. (2) The effect size should measure at least one of the following related variables: crop yield, financial profitability, biodiversity, pollination, pest control, water regulation, soil fertility, nutrient cycling, carbon sequestration, and climate regulation (for more details on the variables, see Supplementary Table 8). (3) The number and list of original studies should be reported. (4) The study should also include information on the duration of diversification practices. In the case of multiple response variables from the same study, we ensured that each response was associated with an independent factor without the possibility of redundancy from one response to another and causing pseudo-replication of the underlying data and effects. Furthermore, some variables could be used as indicators of more than one category of response variables. In this case, we put the variable as a non-repeated response for the main or directly related response variable and as a repeated response for the other response variables. For instance, pollinator richness is considered in both pollination and biodiversity, but we put it in pollination as a non-repeated response and in biodiversity as a repeated response.

The above criteria narrowed the list of studies to 192 meta-analyses for data extraction (Supplementary Data 1). We extracted the mean and variance of effect sizes expressed in log response ratio (lnRR), response ratio (RR), and percentage of change and noted the qualitative effect (i.e., whether it was non-significant, significantly positive, or significantly negative). We used Plot Digitizer (http://plotdigitizer.sourceforge.net/) to extract data from the figures. We converted the effect sizes from RR and percentage of change into lnRR to obtain a uniform measurement. When the effect size was presented in standardized mean difference (SMD) such as Hedges’ d or other effect sizes that could not be converted into lnRR without original data, we only extracted the trend (negative or positive) of the effect size as well as the statistical significance, that is, negative, non-significant, or positive. For each effect size, we noted the duration (years) of agricultural diversification practices, as well as the number of original comparisons. In cases where the number of replicates was not reported, we set the number of replicates to 1.

In this study, the duration represents the number of years during which agricultural diversification was practiced. When the duration of practice was provided as an interval in the meta-analysis results, we examined whether the authors provided the data with the exact duration of the practice associated with each effect size included in the analysis. When the data were provided, we calculated the effect sizes for all the durations provided using the same method described in the paper. When the data were not provided with exact duration or not provided at all, we considered the mean duration (e.g. for 5–10 years, the duration would become 7.5 years). When the duration was indicated as “>x” we added two years, as an educated guess since we did not find any suitable guidance from the literature, so >10 years would become 12 years to have a single duration for the corresponding effect size.

We categorised each response variable (effect size) into crop yield, financial profitability, biodiversity, pollination, pest control, water regulation, soil fertility, nutrient cycling, carbon sequestration, and climate regulation (see Supplementary Table 8). We also categorised agricultural diversification practices into six categories: non-crop diversification, crop diversification, organic amendment, reduced tillage, inoculation, and organic farming (Supplementary Table 9).

A strong synthesis should not repeat the original studies to guarantee that the effects sizes from all studies are independent. We avoided including meta-analyses with similar underlying data by first running an independence analysis of our 192 studies based on the references of their original studies. We calculated the percentage of original references shared by papers and identified 24 papers with more than 30% of shared original studies. We examined the focus and duration of the effect sizes in these studies. When two or more papers had the same response variable within the same duration, we retained the effect sizes from the most recent study. Using these criteria, we excluded eight papers, resulting in 184 meta-analysis papers with 6,741 effect sizes from 17,989 studies and 192,817 comparisons (Supplementary Fig. 7). We evaluated each study’s quality based on the methods used and the reported results (for more details, see Supplementary Table 10), as these aspects determine the accuracy of the first-order meta-analysis results, which then affect the outcomes of second-order meta-analyses models67. More specifically, we assigned a score of 1 or 2 to each of 8 different information from the paper based on their absence or presence, respectively. For each paper, the total score could vary between 8–16 and we considered a paper as low quality when the score was from 8 to 13 and high quality when from 14 to 16 (see supplementary Table 10).

Data analysis

We synthesized the effects of agricultural diversification on various response variables across all agricultural diversification practices combined and for each practice individually. We used second-order meta-analysis based on hierarchical meta-regression models, weighted with the number of comparisons behind each effect size to account for the number of observations behind each effect size.

For each model, we first performed null model comparisons to determine the best random effects structure for each category of the response variable. For this, we compared the AIC of null models with different random effects structures based on effect size IDs and study IDs (see Supplementary Table 11) to account for heterogeneity or between-study variations from the primary studies and effect sizes76. Specifically, study ID as a random effect would, for instance, account for differences in how yield is measured in different crops under different management in different studies. Then, we ran models predicting the effects of diversification on crop yield, financial profitability, biodiversity, pollination, pest control, water regulation, soil fertility, nutrient cycling, carbon sequestration, and climate regulation individually, without a time moderator (hereafter moderator), to have reference values for non-temporal syntheses. We further ran the same models by adding the duration of diversification practices as a moderator for both overall and individual agricultural diversification practices. In addition, we performed another model selection based on AIC to determine whether the relationship between our response variable and durations of practice follows a linear, quadratic, cubic, or quartic relationship (see Supplementary Table 11), using the best random effects selected from null model selection. We used the metafor r-package77 to run the meta-regression models, extracted the model results with the r-package orchaRd78, and used ggplot2 for visualization79, in R version 4.4.380.

We conducted several tests to detect potential biases in our models. First, we used a funnel plot to visually detect the residuals of the models against the inverse of the standard error (Supplementary Fig. 8) with a potential publication bias when the intercept significantly deviated from zero81. We ran Egger’s regression tests to detect asymmetry of funnel plots (Supplementary Table 12), coupled with fail-safe N analyses based on the Rosenthal method (Supplementary Table 13). We tested the influence of the studies based on the standardized residuals and hat values (Supplementary Fig. 9), where the studies with high influence were those with twice the average hat value and with high standardized residuals82. Our tests did not reveal any significant issues associated with publication biases or outliers.

To ensure that our results were robust, we performed several sensitivity analyses to compare the outcomes. First, given that we included repeated effect sizes in the response variables, we ran a second-order meta-analysis using a dataset without any repeated responses. Second, since study quality might create some biases, we ran the same analysis, but this time with the dataset of high-quality studies only. To obtain results from the most conservative analysis, we ran a third type of model with high-quality studies and without repeated responses. These models were run for both the overall effects of diversification and the duration of practices. Overall, the results from these different datasets did not show any significant differences from our main results (i.e., the full dataset; Supplementary Fig. 10). For carbon sequestration and nutrient cycling, excluding repeated variables from full dataset and from high-quality studies resulted in non-significant effects from 50 years, possibly because of data limitations, leading to a wider range of confidence intervals. It is worth noting that all estimates remained positive. Therefore, for the coherence of data analyses, we used the full dataset throughout.

We also performed several sensitivity analyses to check the robustness of our results. First, we determined whether including studies that report imprecise duration could change the outcome of our results. For this, we ran a separate model with only the effect sizes that had exact duration. When compared to the results of the full dataset, we found no significant differences between outcomes. Water regulation was the only exception for which the confidence interval overlaps with 0 from 30 years onwards (see Supplementary Fig. 11), possibly due to data limitations. It is worth noting that 95 % of the effect sizes included in the full dataset for the second-order meta-analysis had precise durations. Second, we ran an analysis excluding the single data point at 100 years for crop yield to assess its impact; third, we ran a separate analysis of the effects of diversification on soil fertility without soil pH. Both analyses showed no significant differences compared to the results from our main models (see supplementary Fig. 12).

We analysed the variation over time in the trade-offs associated with crop yield. We selected studies that tested diversification effects on crop yield and other services simultaneously and categorised each effect size as respective, win, or lose if the effect size was positive or negative, regardless of their statistical significance. This yielded four standard trade-off outcomes: win_yield - win_Service, win_yield - lose_Service, lose_yield – lose_Service, and lose_yield - win_Service. We then ran hierarchical regression models predicting these outcomes based on a multinomial distribution using the nnet r-package83, and we used the effect size of the crop yield paired with services, nested within the study ID, as random effects. We included the average of the original comparisons of crop yield and paired services as weights to account for the underlying number of comparisons. We ran the models for crop yield with all the effect size categories combined first, and then models with the individual categories of effect size categories with the duration of the practices as a moderator.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This second-order meta-analysis analyzes the effect of crop diversification on ecosystem services. The originality lies in considering the temporal effect. The subject is interesting, but I have very strong reservations about how it has been treated:\n1. On the definition of diversification: The authors include diversification in the title but actually study 6 types of practices (see Supplementary Table 3). I also have questions about the definition of categories and the practices included in each category. This leads to a lack of precision on categories and their limits, and a risk of mixing quite different things (e.g., landscape and animal-crop symbiosis). Even though this table is based on Tamburini et al., 2020, recent research has shown the limitations of this categorization and approach, which can obscure very different effects.\n2. On the definition of categories for outcomes: There may be the same type of problem, but even more pronounced. In the categories, I observe variables such as Pollinator diversity; Pollinator richness; Predator richness. Shouldn't Pollinator diversity and Pollinator richness be associated with pollinators Category? Or are they associated with two categories, but this raises questions about the over-representation of this data (and is not transparent in the text). In the Water regulation category, I observe soil bulk density. This seems quite distant. You may not be obliged to take the variables and make them fit into your categories. Some may refer to other dimensions of ecosystem services. In nutrient cycling, I observe springtail diversity?? In climate regulation, I observe plant N uptake, N2 fixation, Soil NH5?? in financial profitability, I observe the variable production cost. Have you reversed the sense of effect sizes? Because mixing production cost and profitability makes no sense). I think this categorization poses a problem.\n3. Is the search string complete regarding all the practices you study? I'm not sure.\n4. For the identification of papers, did you also list second-order meta-analyses on these subjects? There are now at least 5-6, which combine several hundred meta-analyses. Official guidelines for conducting meta-analyses recommend using these \"snowball\" techniques.\n5. We have no information on the reproducibility of the procedures. Several examples: have you conducted a kappa test to see if different reviewers arrived at the same articles retained or rejected? The database was not accessible to me, yet the quality of this work is mainly linked to the quality of the extraction, coding, so it is essential that this database be accessible to carry out transparent and reproducible work (and useful for other researchers). How did you obtain the primary studies? How did you distinguish if they were different (by their DOI? but what about thesis or other grey literature?,...)? Having faced these questions myself, I know they are not trivial and sometimes choices have to be made, on which transparency should be ensured. It's great to study the quality of meta-analyses, but where do the criteria come from (articles have specifically studied these questions and proposed criteria)? The definitions in Supplementary Table 4 are not precise enough for me + I wonder if some criteria do not contradict the criteria for inclusion/exclusion of articles. on how to extract and code the effect size: have you reversed the direction for certain categories?\n6. I am skeptical about the choice of coding the duration of the experiments: Adding two years when meta-analysis authors use \">\" is a very large assumption (Do some authors also use '<'?). Taking the median to classify them is another. There is a lot of variability within a meta-analysis in durations, and I find it difficult to conceive the scientific relevance of being able to make precise scientific calculations from this information. You could have retrieved the meta-regression that analyzes the effect of time since the implementation of the diversification statgie ; you could have applied a robust methodology to synthesize this data. But honestly, here, as we have a lot of \"hidden\" variability at the base + your choice adds heterogeneity, I have only very moderate confidence in the analyses to indicate an effect precisely (and combined with the imprecision of the definition of practices and outcomes, this complicates things even more).\n7. The sentences in the results are sometimes awkward at best, and exaggerated at worst. Several examples (not exhaustive): the numbers in the abstract are not valid. It is not reasonable to show increases of over 2000%. This is partly related to a methodological problem (see next point). You claim that there is no publication bias, whereas the tests do not all seem to say that (I would not recommend fail and safe numbers which seem statistically weak in the majority of cases: you have practically no chance of detecting publication bias with this method, especially if you have numerous data). For some plots, you only show fitting curve (and not raw data). is it maybe because you have not enough point ?\n8. Statistical problems. You use a linear model to analyze the effect of the time since conversion into diversified systems. Have you tested other models (non linear? smoothing, GAM, ...)? The linear model is based on strong assumptions that may not be respected here. Using values at 50 years to analyze the effects even though there are very few points in this range seems problematic. You are going out of the model's validity range. I think there is also confusion between prediction and estimation (seen in some phrases of the materials and methods).\n9. Your paper is relatively poorly anchored/compared to other second-order meta-analyses published on the same subject. You have preferred to base your references on big concepts/big ideas, but there is a real lack to properly situate your work in relation to what exists, what it brings, to confront different or similar results.\n10. I noticed a number of small typos, typos.\nIn summary, the idea is interesting, but the way it is treated (categorization, choice of models, choice of second-order meta-analysis), the imprecision (methodology not transparent and reproducible, database not accessible), and the exaggerations and lack of confrontation with existing literature, give me little confidence in the results.", "reviewer_2": "This is an impressive review of existing studies on diversified cropping systems. The statistical methods are robust and it should make a useful contribution to the ongoing discourse around sustainable intensification, agroecology and regenerative agriculture. There are, however, some limitations to the study that need to be addressed. These are detailed in the annotated manuscript and summarised here.\n1. As alluded to above - this study is highly relevant to agroecology and regenerative agriculture, yet neither of these systems/movements is mentioned in the paper. In particular, there should be some mention of regenerative agriculture as it sets the context for the current discussion around these issues.\n2. There are limitations when discussing crop yield as a response variable - studies normally use data from a single year to compare yields of the same crop under different management systems. But in diversified systems where the crop rotation is different, it is more appropriate to consider yields of some normalised/standardised variable over the whole rotation. This is not simple to do and I don't expect the authors to do this in this study, but they do need to acknowledge this limitation.\n3. Another limitation relating to yields concerns the land not in production where non-crop diversification measures are used. This also needs to be acknowledged, e.g. that the total land area required to implement these methods needs to be larger so on a landscape scale, yields per hectare may not be as high.\n4. Another scaling issue concerns organic amendments - in many studies positive effects will be reported for rates of application that could not be scaled to the landscape level i.e. organic matter from a large area is being concentrated on a specific plot of land to achieve benefits, but this approach could not be scaled to the landscape. This also needs discussing.\n5. The authors state that they couldn't find studies on climate regulation in organic systems, but Gattinger et al have done reviews on this topic. Please check if these are included in your study.\n6. The data for inoculants is weak and I suggest excluding this from the study. I am not convinced that all appropriate studies were sourced. I also don't consider inoculants a diversification strategy; they are an input substitution approach that is often applied within non-diversified systems of production.", "reviewer_3": "The manuscript “Decades matter: Agricultural diversification increases financial profitability, biodiversity, and ecosystem services over time” quantifies long-term effects of agricultural diversification practices on ten social-ecological outcomes based on 184 meta-analyses and 4,260 effect sizes, covering a time span of 50 years. The authors use hierarchical meta-regression models to estimate effects of diversification, and subsequently a multinomial hierarchical model to estimate trade-offs associated with crop yield and ecosystem service outcomes.\nI commend the authors for the quality and innovativeness of the manuscript. Addressing the temporal dimension of diversification effects on social-ecological outcomes is urgently needed information for scientists, decision-makers, farmer, and the public alike. Before I can recommend the manuscript for publication, I would kindly ask the authors to address my comments/concerns detailed below.\nMAJOR\n1. I like the set-up of the analysis with first establishing a baseline and then assessing the differences to models with a time moderator. I also appreciate the many sensitivity/robustness checks that have been conducted. However, I have a few questions in that regard:\n1.1 You have assessed publication bias, yet I was wondering about geographic biases as certain regions might covered by more meta-analyses compared to others. Moreover, the potential selection bias of which farms participated in the original studies in diversified practices (random, partial random, or opportunistic sampling) remains unclear.\n1.2 Related to the above, the effect of diversification on social-ecological outcomes does not occur in vacuum, but in landscapes and social contexts. Hence, I can imagine that effects, especially long-term effects, might differ in different regions. Would it be possible to test if, for example, landscape composition moderates diversification outcomes?\n1.3 I appreciate the efforts to exclude meta-analyses with similar underlying data, yet (to me knowledge) meta-regressions still suffer from the problem of multiple testing (Type 1 error). Hence, correcting for this, e.g., by using Bonferroni methods, would be advisable.\n2. The temporal depth of the study is remarkable, but this also creates some problems.\n1.1 How did the authors control for potentially changing social-ecological conditions over time (e.g., climate change)? The effect sizes inherently include such changes, which makes it difficult to distil the pure effect of the practice/strategy.\n2.2 Several outcomes have only few data points for long-term effects (e.g., beyond 20 years). Although this is reflected in the wide CIs, this arguably affects the direction of the effect. A good example is pollination, for which 3 data points define the direction of the effect. Without those data points, the effect would be neutral (or even negative).\n3. Although I understand the motivation for calculating trade-offs and synergies between ecosystem services and crop yields, I think this introduces a clear argumentative direction in the paper (profits vs. conservation). Why did the authors refrain from assessing joint effect strengths of diversification practices/strategies on all paired outcomes (as there might be trade-offs between certain outcomes that currently go unnoticed and unreported)?\nMINOR\n4. I was wondering whether the two social-economic factors are sufficiently different from each other, as I would assume that yields and financial profitability are quite strongly related in many world regions. Alternative examples would be human well-being and/or food security, and I suggest including such more human-focused outcomes in the analysis, if this is possible based on the meta-analyses used.\n5. Your literature search followed the PRISMA review protocol, and I suggest to clearly state that in the methods section.\n6. From what I understand from the methods section, the authors did not assess the effect of combined practices on social-ecological outcomes. Was there a reason for this, as this would have been quite interesting?\n7. The abstract should not contain references.\n8. Please revise the keywords as they should not repeat words used in the title. Also, you might want to consider using the term Nature’s Contribution to People, as suggested by the IPBES in their most recent report. Perhaps also consider adding terms such as resilience.\nDETAILS\n9. Line 20-21: I suggest rephrasing: ‘Many studies have confirmed agricultural diversification with practices such as intercropping, organic farming, or soil inoculations, as a suitable pathway…’.\n10. Line 23: Which method did you apply to quantify the effects? I assume some sort of meta regression?\n11. Lines 25-28: It is unclear which diversification practice(s) this sentence refers to. Any practice, a specific practice, or a certain combination of practices? Please clarify.\n12. Line 27 & 29: Increases of 2800% and 2000%, respectively seem extremely high. I do not doubt these values, but I assume that they are the result of the small number problem with respect to relative increase of very small values over time. Perhaps report the effect sizes as well?\n13. Lines 28-29: are those the same benefits as in the previous sentences? Do all of them show a positive response to practices?\n14. Line 30: Which other services? This is not defined and needs an explanation. Also, what happened after those 25 years? Did yields go down?\n15. Line 33: I am not sure if I agree with the statement that the results actually provide evidence for allowing diversification upscaling, as this was not analysed (they certainly encourage efforts to upscale, though).\n16. Lines 43-45: I suggest including the recently published paper by Rasmussen et al (dx.doi.org/10.1126/science.adj1914), which provides evidence of joint environmental and social benefits from diversified agriculture.\n17. Line 64: Do you mean food security? Otherwise, reformulate into “addressing food insecurity”?\n18. Line 73: Please provide more details of what mean with ‘long-term effects’, e.g. the minimum number of years between assessments.\n19. Line 75: Do you mean non-agricultural biodiversity?\n20. Lines 75-77: Do the meta-analyses contain information about reduced environmental externalities? If yes, this would be interesting to include as well.\n21. Lines 84-85: Well, it did not reduce yields on average…it surely does in some meta-analyses.\n22. Lines 87-88: This statement hinges on the conditions that stabilizing yields and increased profits occur in the same regions/on the same farm. As you report average effect, I would caution this.\n23. Lines 104-105: What about transitions that are not at the extremes of the gradient? Please see the paper by Beckmann et al. 2019 (https://doi.org/10.1111/gcb.14606).\n24. Line 107: ‘More sustainable’ or ‘less environmentally impactful’.\n25. Lines 174-177: Why is that? Why do yields lose after time when ecosystem services win?\n26. Line 225: Perhaps link to LTSER research here?\n27. Line 237: This is not entirely true…on average it does not result in yield declines.\n28. Line 241-242: Well, if awareness is increasing in NA and Europe, shouldn’t these also be the regions where efforts should be enhanced (notwithstanding the fact that those regions are largely characterised by industrialised and specialised agriculture)?\n29. Line 293: Do you assume not significant changes as ‘neutral’? To me, there is a difference between both categories and I would suggest to re-label the ‘neutral’ class to ‘not significant’.\n30. Line 302: Your example would be the mean duration, correct?\n31. Line 303: Why two years and not one year (or five years)? The choice seems arbitrary and needs at least a justification, ideally a sensitivity analysis, to quantify the influence of this choice on the results.\n32. Line 312: … effect sizes from all studies are independent.\n33. Line 313: … independence analysis for our 192 studies…\n34. Line 325: What do you mean with ‘overall’? Is this the total number of diversification strategies and practices? Or is this reflecting if studies cover any practice? Please clarify.\n35. Font sizes in Figure 2 are very small, which makes the figure very hard to read."}, "author_response": ""}, {"round": 2, "reviewer_comments": {"reviewer_1": "This manuscript investigates the long-term effects of agricultural diversification practices on socioeconomic and environmental outcomes using a second-order meta-analysis. The authors provide a compelling case for the benefits of diversification, particularly in financial profitability, biodiversity, and pollination, supported by quantitative analysis. The focus on temporal trends is an important contribution to understanding the sustainability of these practices.\nThe authors’ responses to my previous comments are satisfactory, and I appreciate the clarifications provided. My review is therefore divided into two sections: first, comments on the revised manuscript for the next version, and second, feedback on the authors' responses to my previous requests.\n1. \"Financial profitability increased significantly by 189% over 20 years compared to short-term effects (< 3 years, Supplementary Fig. 2, Supplementary Table 2).\"\no Is it correct to interpret this as meaning that the effect is approximately doubled between 3 years and 20+ years? To avoid confusing the reader, I suggest specifying the value at 3 years.\n2. \"Our analyses also showed that the probability of non-significant effects of agricultural diversification on crop yield increased over time.\"\no Could this be phrased more clearly and simply for the reader?\n3. \"These findings show that the crop yield of diversified farming systems stabilizes over time.\"\no Does your analysis fully support this statement? While an increased probability of non-significant effects may suggest that yields of diversified and non-diversified systems converge over time, this could also be due to greater heterogeneity or increased variability in study outcomes. The figure on crop yields (Fig. 1) does not seem to show a reduction in variance over time, does it?\n4. For the crop yield data, I am still uncertain about the unique data point at 100 years.\no Although it might not affect the overall results, did you test the influence of this point?\n5. For biological communities:\no \"Agricultural diversification significantly increased biodiversity and pollination benefits by 64% and 629%, respectively, over 20 years (1093 years for pollination; Fig. 1, Supplementary Table 2).\"\no The phrasing could be improved for clarity regarding the duration.\n6. Soil quality and climate change mitigation effects:\no \"All effect sizes of soil quality and climate change mitigation, except for climate regulation, were significantly positive.\"\no The formulation could be adjusted to clarify that it refers to the mean effect sizes being positive, not all individual effect sizes across the meta-analyses.\n7. Crop diversification effects on pest control services:\no \"Crop diversification effects on pest control services did not change significantly over time, and there was no significant difference compared to conventional agriculture for up to 50 years.\"\no This seems somewhat contradictory to other second-order meta-analyses, such as those in Science and Global Change Biology.\nFeedback on the Authors' Responses:\n1. R1-1:\no Tamburini et al. is not the only second-order meta-analysis offering this type of classification.\n2. Supplementary Table 8:\no There appear to be some errors in the variables highlighted. For example, SOC is present in multiple lines but not highlighted.\no Also, a minor clarification: Is it indeed beneficial to increase or decrease pH for soil fertility?\n3. R1.2:\no Your response addressing ambiguous variable categorization is appreciated. The inclusion of sensitivity analyses, where variables are grouped in all potential categories, adds robustness to the methodology.\n4. R1.3:\no The clarification regarding the NH5 typo and its removal from the climate regulation category is noted and appreciated. The explanation about reversing the sense of effect sizes for undesirable outcomes, such as production costs, adds clarity. Highlighting the variables with this reversal in Supplementary Table 8 is helpful for transparency.\n5. R1.4:\no No further comments, this is acceptable.\n6. R1.5:\no Perfect.\n7. R1.6:\no This is fine, but what about kappa or consistency in the extraction procedure?\n8. R1.7. I still could not find the data or scripts for the analyses. Could you please provide access to them?\n9. R1.10:\no Well done, this must have been a significant amount of work. You have revisited all the primary studies. If you had access to the databases from the meta-analyses and completed them, wouldn't you now have all the data needed for a first-order meta-analysis?\n10. R1.12:\no OK, thank you.\n11. R1.13:\n• Perfect, thank you.", "reviewer_2": "This study is impressive and could have a big impact, so it is extremely important that the limitations of agricultural diversification approaches are acknowledged and openly discussed. Currently, the concluding statement in the abstract is too one-sided, suggesting that “agricultural diversification can be upscaled for long-term socioeconomic and nature-positive outcomes and, ultimately, a global food system transformation.” This statement does not acknowledge two key limitations: I referred to these in several places in my comments but I am not satisfied with the responses provided.\nFirst of all, studies that report crop yields in diversified versus more conventional systems, normally compare yields for a single species in a single year. There is not normally any attempt to account for the frequency that each crop appears in the rotation. The authors repeatedly refer to their statistical approach e.g. using study ID as a random effect suggesting that this will address this problem. They also suggest that the authors of the studies they use will have accounted for the “differences in yield measures in the first place”, but this is not normally done. The authors seem to have missed my point here and I hope they can address this really important limitation of the dataset and also the limitation of agricultural diversification approaches: diversified systems normally result in a lower frequency of food crops in rotation so that even where yields for a specific food crop appear to be similar for diversified and intensive rotations (because comparisons are normally made for a single year), at a rotational scale, total calories produced is often lower. This means that at a food system level, some moderation of diets is usually required when production systems using “agricultural diversification” are modelled to achieve adequate nutrition for a society. This needs discussing and the conclusions of the study need moderating to acknowledge this concern.\nSecondly, there is no acknowledgement that most studies reporting positive effects from applications of organic amendments are not “scalable” i.e. the rates of amendment applied are in excess of the quantity of material that could be produced from that area of land. This means that there simply is not enough organic material available to supplement the land at these rates, which limits the potential of this practice unless sources of organic matter external to the farming system can be sourced. This issue is also not adequately addressed below.\nBoth of these factors mean that agricultural diversification and its benefits will not be easily upscaled, as stated so boldly in the abstract.\nThe comments below mostly relate to these two issues. They really need discussing in the paper and the statements about possible outcomes from agricultural diversification need to be moderated and presented in a more nuanced way.\nR2-3 The rebuttal on the point about differences in crop yields in studies not normally taking into account the differences in the rotation i.e. single year results from diversified systems. Still needs to be discussed in more detail, in the discussion section ideally. The explanation that these will have been taken into account in the original studies is not credible.\nR2-4 The response somewhat addresses this issue (of land use for more extensive, diversified systems).\nR2-5 The issue of scalability of practices that use organic amendments is still not adequately discussed. From the answer I am not convinced that the authors have understood my point, or if they have, they need to be more explicit about the limitations of approaches that require organic amendments to be applied at rates in excess of the production capacity of the land. Please try again to explain this more clearly.\nR2-6 The response is not adequate. The statement at line 243-244 is not correct. There is an article by Skinner et al. (The impact of long-term organic farming on soil-derived greenhouse gas emissions) about GHG emissions from organic farming systems. Is this included in the meta-analysis?\nR2-7 I still don’t agree that inoculants are a diversification strategy and would prefer to see this response variable removed from the study.\nR2-14 As stated above, I don’t think you can assume that the primary studies have taken into account issues like rotational diversity and the cost across rotational time scales of diversification. I also don’t think the issue of applying organic amendments at rates higher than the production capacity of the land is adequately discussed and I encourage the authors to consider both of these issues in more detail in the discussion part of the paper.\nR2-15 Still issue with not addressing that the area of land needed to produce the amendments needs to be discussed.\nR2-16 A statistical approach is suggested to account for the problems with studies that report crop yield by species (thus not accounting for non-productive years in their results). This misses my point; I am only asking that this limitation/issue with crop diversification needs to be discussed. Systems that are more diversified may have smaller areas of food crops when scaled up to the landscape. Most of the major studies on crop yields comparing organic with conventional have reported the yields for a single crop, not taking into account the proportion of time that it is growing in each system.\nR2-18 My point here relates to the issue raised in several comments above and still not adequately discussed. See response to R2-16 above for my concern; this needs to be addressed. Meta-analyses comparing organic with conventional (e.g. Seufert et al 2012, De Ponti et al ) report yields for a single species in a single year and do not take into account the fact that in diversified systems food crops may be grown less frequently in a rotation than for more intensive systems.\nR2-23 As already stated above, I am not suggesting a different analytical approach but I would like to see a bit more discussion on some of the fundamental differences in practices and how that might impact food production at the landscape scale and also effects on the wider food system. Lower yields per unit area in diversified systems and lower areas of food crops being grown at the landscape scale are two likely outcomes of a transition to agroecological methods; this needs to be acknowledged and is important to discuss.", "reviewer_3": "Thank you for submitting the revised version of your manuscript “Decades matter: Agricultural diversification increases financial profitability, biodiversity, and ecosystem services over time”, along with detailed responses to the reviewer comments. I appreciate the effort you have invested in addressing the feedback of my co-reviewers and myself, esp. determining the relationship between effect sizes and duration, as well as going back to the original studies, recalculating effect sizes for those that did not report exact durations, and re-running the analysis.\nAfter going through the responses to comments, I agree with most of them and can follow their argumentation. However, many of your responses are purely argumentative and did not result in changes in the manuscript. I would like to detail this (and additional comments) in the below.\n1) Your response to my comment on geographic and selection bias is understandable, yet I would have appreciated that these biases were included and discussed in the manuscript (along the lines of several comments made by Reviewer 2), rather than just to me. Moreover, I am not fully convinced that extracting geolocations from the original studies would not have been possible, esp. considering that you extracted effect sizes from those studies. A possible approach could be to count the number of studies per country, esp. if exact geolocations are difficult to extract.\n2) Along those lines, I would have appreciated a discussion of the effects of landscape contexts as well as changes therein (e.g., climate change) on socio-ecological outcomes of diversification, esp. considering the importance of the former as shown in Rasmussen et al. (2024). Even though original studies are lacking that consider both duration and climate variables (or other changing context variables, such as economic conditions), not being able to address this is still an important caveat of the analysis and should be discussed.\n3) I also think that not addressing combined effects of different practices over time on socio-ecological outcomes could and should be discussed.\n4) In your response R3-27, you argue for “targeted meta-analysis such as the one by Beckmann et al. 2019 and local empirical studies for instance on simultaneously testing social and environmental variable changes”. This does not address my comment, which targeted the statement on the benefits of diversification for simple monocultures. This only looks at one part of the intensity gradient (highly intensified), but neglects areas managed with lower intensity. Such area, however, are crucial for planning, considering their sensitivity to intensification regarding yields and biodiversity.\n5) Lastly, and importantly, I had difficulties with the way your report your quantitative results on changes in outcome variables. This pertains to the fact that a) your report values for some outcomes (e.g., 189% increase for financial profitability) but none for others (e.g., soil quality and climate change mitigation), and b) the numbers do not fully agree across manuscript sections. For example, you report in lines 117-118 “financial profitability, biodiversity, pollination, soil fertility, nutrient cycling, and carbon sequestration being 22% to 427% higher after 20 years”. However, in lines 92-93 you state “for biological communities, agricultural diversification significantly increased 92 biodiversity and pollination benefits by 64% and 629%, respectively”. Moreover, in the abstract (lines 25-25) you report “financial profitability, biodiversity, pollination, soil quality, and carbon sequestration from 37% to 189% over 20 years”. Why do the value ranges differ?"}, "author_response": "Point-by-point responses to comments\nJournal: Nature Communications\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nR1-1. This second-order meta-analysis analyses the effect of crop diversification on\necosystem services. The originality lies in considering the temporal effect. The subject is\ninteresting, but I have very strong reservations about how it has been treated:\n1. On the definition of diversification: The authors include diversification in the title but\nactually study 6 types of practices (see Supplementary Table 3). I also have questions about\nthe definition of categories and the practices included in each category. This leads to a lack\nof precision on categories and their limits, and a risk of mixing quite different things (e.g.,\nlandscape and animal-crop symbiosis). Even though this table is based on Tamburini et al.,\n2020, recent research has shown the limitations of this categorization and approach, which\ncan obscure very different effects.\nThank you for this important comment. We understand that you are essentially referring to\nthe high level of abstraction of our second order meta-analysis and the need to categorize the\ndata obtained.\nWe made the conscious methodological decision for a second-order meta-analysis, because it\nallows us to make high level inferences based on the vast amount of detailed data. We,\ntherefore, also had to accept that – while very important – we cannot aim to provide details\non the plethora of diversification practices.\nMoreover, our methodological choice, necessitates stringent categorization of the obtained\ndata. We decided to use now widely used categories as Tamburini et al. (2020) in our study.\nThis is, because it is the only second-order meta-analysis study that includes different\ncategories of agricultural diversification, and we think that it is best to use existing categories\nto make different results comparable instead of re-inventing new categories for every study.\nTo make our categorization choice repeatable and explicit, we have now included the\ndefinition of the each category in Supplementary Table 8.\nbut even more pronounced. In the categories, I observe variables such as Pollinator\ndiversity; Pollinator richness; Predator richness. Shouldn't Pollinator diversity and\nPollinator richness be associated with pollinators Category? Or are they associated with\ntwo categories, but this raises questions about the over-representation of this data (and is\nnot transparent in the text). In the Water regulation category, I observe soil bulk density.\nThis seems quite distant. You may not be obliged to take the variables and make them fit\ninto your categories. Some may refer to other dimensions of ecosystem services. In nutrient\ncycling, I observe springtail diversity??\nWe agree with the reviewer that several variables are ambiguous and can but used as\nindicators for different outcome categories or response variables. For instance, in many cases\npollinator diversity could be part of the biodiversity and pollination category. For all these\nambiguous variables, we decided on one apparent category and then ran two analyses: one\nanalysis where the variable is grouped only in one category and one analysis where the\nvariable is grouped in all possible categories (i.e. repeated grouping). We identify all the\nvariables as underlined that were considered as repeated variables from other categories (See\nSupplementary Table 8). The results of these sensitivity analyses are now presented in\nSupplementary Fig. 8, which does not show any pattern changes depending on variable\ngrouping (see below).\nmodel results from the effect of diversification over time using the i) full dataset to the model\nresults, ii) full dataset without repeated responses, iii) dataset with high quality studies only and\niv) dataset with high quality studies only and no repeated studies. Some variables only show the\nregression slopes for high quality studies and no repeated responses (green): financial\nprofitability - no repeated and only few low-quality studies; pollination - no repeated responses\nand all high-quality studies; pest control - few repeated responses and all high quality.\nWe have now also added more details in the Methods section to further clarify this aspect. See\nLines 329-334. “Furthermore, some variables could be used as indicators of more than one\ncategory of response variables. In this case, we put the variable as a non-repeated response\nfor the main or directly related response variable and as a repeated response for the other\nresponse variables. For instance, pollinator richness is both considered in pollination and\nbiodiversity, but we put it in pollination as a non-repeated response and in both categories as\na repeated response.”\nSoil bulk density is considered as an indicator for water regulation because this variable is\nrelated to soil aeration and water circulation. Specifically, an increase in soil bulk density\ndecreases the total pore space in the soil and reduces available water capacity. However, we\nagree that this variable may be also grouped under soil fertility and is, hence, part of the\nrepeated response.\nSpringtail diversity is categorized under nutrient cycling, because soil microbes are\nresponsible for the decomposition of organic material and ensure soil ecosystem functions\n(see Bardgett & Van Der Putten, 2014; Handa et al., 2014). Furthermore, the majority of\nindicators for nutrient cycling in our study were related to belowground biodiversity,\nincluding springtail diversity, which is essential for the transformation, degradation, and\nstabilisation of organic matter, thus supporting nutrient cycling (see Potapov et al., 2023).\nR1-3. In climate regulation, I observe plant N uptake, N2 fixation, Soil NH5?? in financial\nprofitability, I observe the variable production cost. Have you reversed the sense of effect\nsizes? Because mixing production cost and profitability makes no sense). I think this\ncategorization poses a problem.\nThank you for these comments. Soil NH5 was a typo; this was supposed to be NH4, which is\nnot a GHG and it was, hence, deleted from the climate regulation category.\nWe took the sense of effect sizes into consideration while extracting the data. If the original\nanalysis had not inversed the sign of the variable that is related to non-desirable outcomes in\nagriculture (e.g. emissions, loss, costs, damage), we inversed the variable to facilitate\ninterpretation. We agree with your example that a positive LnRR of production cost is\nundesirable, whereas a positive LnRR for gross income is. Therefore, we multiplied the value\nof production cost by -1 to translate it into the inverse of loss which is then equal to\nprofitability. This approach was also used by Tamburini et al. (2020), and we now put stars on\neach variable for which we inversed the signs of the effect size (See Supplementary Table 8).\nR1-4. Is the search string complete regarding all the practices you study? I'm not sure.\nWe are confident that our search string captures most if not all relevant studies. We used the\nsame research strings as Tamburini et al, 2020 for the practices. We use the search strings\nfrom this study, because the research strings include all the possible keywords for the different\npractices and were developed with world leading experts in a 3-day in person workshop. We\nalso added the word “crop*” as a global search term to make sure that we also find all papers\nthat focus on the effects cropping systems regardless of whether the topic is related to one of\nthe practices mentioned in the other keywords. Thus, our search is comparable and\ngeneralizable.\nthese subjects? There are now at least 5-6, which combine several hundred meta-analyses.\nOfficial guidelines for conducting meta-analyses recommend using these \"snowball\"\ntechniques.\nThank you for this question. Yes, our research strings included both first- and second-order\nmeta-analysis papers. In total, we found 11 second-order meta-analyses and used\n“snowballing”, whereby we ensured that the relevant cited studies were also included in our\nfinal results. We have now included the list of the second-order meta-analyses in\nSupplementary Table 7 and these details in the methods sections (L314-318):\n“We screened the full text of 1,316 papers individually and found 11 second-order meta-\nanalyses papers related to the topic of this study that focused on at least one of our response\nvariables (see Supplementary Table 7). We also screened the papers from those meta-analyses\nand ensured that all studies included in these studies were also included in the remaining\npapers for abstract screening.”\nR1-6. We have no information on the reproducibility of the procedures. Several examples:\nhave you conducted a kappa test to see if different reviewers arrived at the same articles\nretained or rejected?\nThank you for this clarifying comment. We planned the data collection with robustness\nreproducibility in mind. First, we used research strings have been used by other researchers\nbefore and were again thoroughly tested by ourselves to include all relevant studies. Second,\nwe defined the inclusion criteria based on common elements that are reported in meta-\nanalyses to ensure that any researcher would come to the same sets of studies included.\nLastly, we performed quality assessments that other researchers could use to exclude further\nstudies in case of conducting a very conservative meta-analysis.\nR1-7. The database was not accessible to me, yet the quality of this work is mainly linked to\nthe quality of the extraction, coding, so it is essential that this database be accessible to\ncarry out transparent and reproducible work (and useful for other researchers).\nWe have now shared the data and the code used for the analyses privately to allow the\nreviewer to conduct a thorough assessment of the methods we used. Upon publication, we will\nmake the code available to other researchers and keep the data in a private repository. We\nopted to keep data private for now, because they are currently used for other projects.\ndifferent (by their DOI? but what about thesis or other grey literature?,...)?\nAs part of our inclusion criteria, the meta-analysis needed to report the list of primary studies\nincluded in their results. Therefore, for each included paper, we extracted the first-author\nname, year, and title of the studies to ensure that each original study would have a unique\ncode.\nR1-9. Having faced these questions myself, I know they are not trivial and sometimes\nchoices have to be made, on which transparency should be ensured. It's great to study the\nquality of meta-analyses, but where do the criteria come from (articles have specifically\nstudied these questions and proposed criteria)? The definitions in Supplementary Table 4\nare not precise enough for me + I wonder if some criteria do not contradict the criteria for\ninclusion/exclusion of articles. on how to extract and code the effect size: have you reversed the direction for certain categories?\nThanks for this comment on a critical aspect to assure the quality of a meta-analysis. When\nconducting the study quality assessment, we used the standard reporting criteria for\nestimation accuracy of the effect sizes for meta-analyses (see Schmidt & Oh, 2013). For the\nscoring system, we decided to use an already established method used by Tamburini et al.\n(2020). We have now specified these aspects in the Methods section for conducting and\nscoring the quality of studies included in the second-order meta-analyses and cite the\naforementioned reference (L373-381):\n“We evaluated each study's quality based on the methods used and the reported results (for\nmore details, see Supplementary Table 10), as these aspects determine the accuracy of the\nfirst-order meta-analysis results, which then affect the outcomes of second-order meta-\nanalyses models (Schmidt & Oh, 2013). More specifically, we assigned a score of 1 or 2 to each of 8 different information from the paper based on their absence or presence, respectively. For\neach paper, the total score could vary between 8-16 and we considered a paper as low quality\nwhen the score was from 8 to 13 and high quality when from 14 to 16 (see supplementary\nTable 10).”\nRegarding the contradictions of inclusion criteria of studies, we are not sure that we\nunderstand this point. In our view these criteria are independent of one another. Specifically,\nwe set these criteria to ensure that we can analyse the data correctly and to account for any\npossible variation across studies for a robust second order meta-analysis. We would welcome\na more specific feedback about potential contradicting elements in these criteria, so that we\ncan address this comment correctly.\ncertain categories?\nThank you for raising this point. As per our response to your previous comment R1-3. we took\nthe sense of effect sizes into consideration while extracting the data. If the original analysis\nhad not inversed the sign of the variable that is related to non-desirable outcomes in\nagriculture (e.g. emissions, loss, costs, damage), we inversed the variable to facilitate\ninterpretation. We agree with your example that a positive LnRR of production cost is\nundesirable, whereas a positive LnRR for gross income is. Therefore, we multiplied the value\nof production cost by -1 to translate it into the inverse of loss which is then equal to\nprofitability. This approach was also used by Tamburini et al. (2020), and we now put stars on\neach variable for which we inversed the signs of the effect size (See Supplementary Table 8).\nR1-11. I am skeptical about the choice of coding the duration of the experiments: Adding\ntwo years when meta-analysis authors use \">\" is a very large assumption (Do some authors\nalso use '<'?). Taking the median to classify them is another. There is a lot of variability\nwithin a meta-analysis in durations, and I find it difficult to conceive the scientific relevance\nof being able to make precise scientific calculations from this information. You could have\nretrieved the meta-regression that analyzes the effect of time since the implementation of\nthe diversification statgie ; you could have applied a robust methodology to synthesize\nthis data. But honestly, here, as we have a lot of \"hidden\" variability at the base + your\nchoice adds heterogeneity, I have only very moderate confidence in the analyses to indicate\nan effect precisely (and combined with the imprecision of the definition of practices and\noutcomes, this complicates things even more).\nWe appreciate these comments from this reviewer, because it has substantially improved the\nrobustness of our analysis. We have now gone thought all studies that did not report exact\ndurations in the results. If a study provided the original data, we recalculated the effect sizes\nfor each of the durations provided in the dataset by rerunning the analyses following the\nprotocol described in the corresponding paper. This increased the number of studies with\nprecise durations by 40% compared to our original dataset. We then performed a sensitivity\nanalysis to determine whether including studies that report imprecise duration could change\nthe outcome of our results. For this, we ran a separate model with only effect sizes that had\nexact duration only and compared the outcomes with the full dataset, which includes both\nimprecise and precise duration (Supplementary Fig. 9). We have now included these details in\nthe Methods section (L429-436):\n“We also performed a sensitivity analysis to determine whether including studies that report\nimprecise duration could change the outcome of our results. For this, we ran a separate model\nwith only effect sizes that had exact duration. When compared to the results of the full\ndataset, we found no significant differences between outcomes. Water regulation was the only\nexception for which the confidence interval overlaps with 0 from 30 years onwards (see\nSupplementary Fig. 9), possibly due to data limitations. It is worth noting that 95 % of the\neffect sizes included in the full dataset for the second-order meta-analysis had precise\ndurations.”\nWe think that the revision of the dataset and the sensitivity analysis has greatly improved\nrobustness of our analysis.\n7\nof model results from the effect of diversification over time using the i) full dataset and the\nii) dataset with precise duration only.\nworst. Several examples (not exhaustive): the numbers in the abstract are not valid. It is\nnot reasonable to show increases of over 2000%. This is partly related to a methodological\nproblem (see next point). You claim that there is no publication bias, whereas the tests do\nnot all seem to say that (I would not recommend fail and safe numbers which seem\nstatistically weak in the majority of cases: you have practically no chance of detecting\npublication bias with this method, especially if you have numerous data). For some plots,\nyou only show fitting curve (and not raw data). is it maybe because you have not enough\npoint ?\nThanks for these helpful comments, which did make us think more about the presentation of\nthe results. In the Abstract, we have revised the results (L23-26):\n“Diversification increased financial profitability, biodiversity, pollination, soil quality, and\ncarbon sequestration from 37 to 189% over 20 years, and we found no significant effects on\ncrop yield, pest control, and climate regulation”.\nFor our publication bias analysis we do not rely solely on fail-safe numbers, but we ran\nmultiple other analyses to detect the bias, including funnel plots with Egger’s test and outlier\ntests (Marks-Anglin et al., 2020; Viechtbauer, 2020). We understand that there are many ways\nto test for publication biases (see Nakagawa et al., 2022), however, not all of the tests were\ncompatible with our models because of the complexity of the model structure (Viechtbauer,\n2010). Based on the plethora of methods used indicating in conjunction that there is no\npublication bias, we trust that our analysis is robust.\nAs for the plots and the raw data, yes, we only show the raw data on the first graphs to show\nthe original effect sizes included in the models to obtain the fitted values and to highlight the\ndata limitation. We did not show the raw data in Figure 2 to increase clarity of the figure and\ndue to data limitations. For Figure 3, plotting raw data is not applicable, because the results\ncome from multinomial models and, hence, the raw data are categorical values.\nR1-13. Statistical problems. You use a linear model to analyze the effect of the time since\nconversion into diversified systems. Have you tested other models (non linear? smoothing,\nGAM, ...)? The linear model is based on strong assumptions that may not be respected here.\nUsing values at 50 years to analyze the effects even though there are very few points in this\nrange seems problematic. You are going out of the model's validity range.\nThanks for bringing these aspects to our intention. We have now revised our method: we ran\nmodel selection to test whether there is a non-linear relationship between duration and the\ndifferent categories of response variables. For this, we first looked for the best random effect\nstructure by comparing the AIC of the null models, and we performed another model selection\nto determine the nature of the relationship between the effect sizes and the duration, again\ncomparing the AIC of 4 different models with duration as 1) linear, 2)quadratic, 3) cubic, and\n10\n4) quartic terms, using random effects of the best models from the first model selection. We\nthen added the results in Supplementary Table 11, which showed that only pollination and\npest control had a non-linear relationship with the duration of practice. We have added these\ndetails in the Methods section see L398-402.\n“In addition, we performed another model selection based on AIC to determine whether the\nrelationship between our response variable and durations of practice follows a linear,\nquadratic, cubic, or quartic relationship (see Supplementary Table 11), using the best random\neffects selected from null model selection.”\nIn comment R1-11 we describe how we increase reliable durations of effect sizes. With the\nincrease of almost 70% in effect sizes, we are confident that our analyses can give a good\nindication on what is known about these long-term trends.\nR1-14. I think there is also confusion between prediction and estimation (seen in some\nphrases of the materials and methods).\nConsidering prediction and estimation in a statistical sense, the former refers to statistical\ninference based on a subset of a complete sample to make an inference of the entire sample,\ni.e. it is linked to forecasting. Predictor is a synonym often used for ‘explanatory variable’ in\nregression modelling. Estimation is the process of estimating a value from an often-\nincomplete set of observations for a specific purpose.\nWe used the word predictor synonymously of explanatory variables in our regression model.\nIn our models we are predicting the effects based of the fitted values of effect sizes over time\nbased on parameter estimates. We hope that the terminology used in the manuscript is\nclarified now.\nR1-15. Your paper is relatively poorly anchored/compared to other second-order meta-\nanalyses published on the same subject. You have preferred to base your references on big\nconcepts/big ideas, but there is a real lack to properly situate your work in relation to what\nexists, what it brings, to confront different or similar results.\nIn our second-order meta-analysis, we focus on agricultural diversification, which means we\nchose a high-level synthesis tool (sensu our reply to comment R1-1) for a topic that includes\nvarious farming practices. In our comprehensive literature search, we found only 11 second-\norder meta-analyses of farming practices commonly included under agricultural\ndiversification effects. To our knowledge, there is no other second-order meta-analysis that\nanalysed the relationship between the duration of diversification practices and the effects on\nsocioeconomic and environmental variables. However, we now have provided more details\n11\nand references in the Introduction and Results section to embed our work better in the\nexisting literature.\nSee for instance L40-53:\n“Agricultural diversification is based on farming practices, such as crop rotation, agroforestry, or\norganic farming, that purposely integrate functional diversity into production systems to enhance or\nmaintain the services essential to crop production (Kremen et al., 2012). Several syntheses have\nconfirmed the socioeconomic (i.e., yield and profitability) and environmental (i.e., biological\ncommunities, soil quality, and climate mitigation) benefits of agricultural diversification practices for\ncrop production worldwide (Rasmussen et al., 2024; Rosa-Schleich et al., 2019; Tamburini et al., 2020).\nIn Asia, for example, rice crop diversification practices reduce pesticide use and increase biocontrol\nand crop yields (Gurr et al., 2016). These benefits have incentivized stakeholder groups to implement\nagricultural diversification across different countries. In China, agricultural diversification was\nrecently promoted into the country’s major agricultural policies (He et al., 2022). In Europe and the\nUSA, several agricultural diversification practices, such as organic farming and crop/non-crop\ndiversification, are advocated in agricultural policies for biodiversity conservation and climate change\nmitigation (Gattinger et al., 2012; Muneret et al., 2018; Smith et al., 2019).”\nor L207-222:\n“Our analysis also revealed a temporal increase of the lose-lose relationship between crop yield and\npest control services (Fig. 3E). However, the effects of diversification on pest control services were not\nstatistically significant over time (Fig. 1, Supplementary Table 1-2), and the probability of this\noutcome increased over the years (Supplementary Fig. 1). This indicates that diversified farming may\nprovide concomitant benefits to some pest control agents and pests (Tscharntke et al., 2016), which\nleads to some negative effects on crop yield. Use of agricultural technologies could help to reduce such\nundesirable effects of agricultural diversification via the use of pest resistant varieties or more\neffective pest control technologies (Herrero et al., 2020). We also found an increase in lose-win\nrelationship between crop yield and carbon sequestration over time (Fig. 3I). This indicates that with\nincreasing effort for carbon capture, farmers face yield decrease, probably due to constraints\nassociated with diversification practices such as smaller areas to plant crops in the case of non-crop\ndiversification (Rasmussen et al., 2024). Thus, climate financing for diversified agriculture ought to\nconsider for such carbon-yield trade-offs and requires even more advocacy for diversification and\nagroecology at climate summit discussions.”\nR1-16. I noticed a number of small typos, typos.\nWe have revised the text and tried to correct all the typos.\nR1-17. In summary, the idea is interesting, but the way it is treated (categorization, choice\nof models, choice of second-order meta-analysis), the imprecision (methodology not\ntransparent and reproducible, database not accessible), and the exaggerations and lack of\nconfrontation with existing literature, give me little confidence in the results.\nshare anonymised data and the R-script for full transparency of the methods and analyses we\nused. Moreover, we toned-down certain aspects of our results. Lastly, however, the lack of\nsimilar syntheses limited us to compare our results with other non-long term syntheses\npapers. We hope that our responses to your comments but more importantly the thorough\nrevision of the dataset, analyses and the writing could convince you that our work is robust\nand worthwhile publishing in Nature Communications.\nReviewer #2 (Remarks to the Author):\nGeneral comments\nR2-1. This is an impressive review of existing studies on diversified cropping systems.\nThe statistical methods are robust and it should make a useful contribution to the ongoing\ndiscourse around sustainable intensification, agroecology and regenerative agriculture.\nThere are, however, some limitations to the study that need to be addressed. These are\ndetailed in the annotated manuscript and summarised here.\nWe thank the reviewer for these positive comments. Please find our responses to the general\nand detailed comments below:\nR2-2. As alluded to above - this study is highly relevant to agroecology and regenerative\nagriculture, yet neither of these systems/movements is mentioned in the paper. In\nparticular, there should be some mention of regenerative agriculture as it sets the context\nfor the current discussion around these issues.\nThank you for this important point. We agree that both agroecology and regenerative\nagriculture are important approaches that are both relevant to make the case for our work.\nWe have included agroecology and related terms in the new keywords (also following your\n(R2-8) and Reviewer 3’s (R3-12) suggestion).\nWe have also included a link to agroecology in the Introduction (sensu R2-9) See L38-40:\n“Transforming the global food system requires nature-positive production systems (Hodson et\nal., 2021; von Braun et al., 2021) using agroecological farming methods based on agricultural\ndiversification practices to achieve sustainable food production (FAO, 2019; HLPE, 2020;\nWezel et al., 2020).”\nAlthough we agree that our work can be linked to regenerative agriculture, we also concur\nwith robust scientific literature that regenerative farming is one of the recent – somewhat\nconfusing terminologies that is encompassed within agroecology (see Bless et al., 2023). We,\ntherefore, decided to not mix both terminologies in our manuscript.\nnormally use data from a single year to compare yields of the same crop under different\nmanagement systems. But in diversified systems where the crop rotation is different, it is\nmore appropriate to consider yields of some normalised/standardised variable over the\nwhole rotation. This is not simple to do and I don't expect the authors to do this in this\nstudy, but they do need to acknowledge this limitation.\nThank you for this comment. In our second order meta-analysis, we include meta-analyses\nbased on empirical studies that compare simplified with diversified practices. These empirical\nstudies would have accounted for such differences in yield measures in the first place. Overall,\nthese differences in how yield is measured in different crops under different management in\ndifferent studies can be accounted for by including study ID as random effects in our second\norder meta-analysis. We have specifically included this detail in our Data Analysis section\n(L388-394):\n“For each model, we first performed null model comparisons to determine the best random\neffects structure for each category of the response variable. For this, we compared the AIC of\nnull models with different random effects structures based on effect size IDs and study IDs (see\nSupplementary Table 11) to account for heterogeneity or between-study variations from the\nprimary studies and effect sizes (Schmidt & Oh, 2013). Specifically, study ID as a random effect\nwould, for instance, account for differences in how yield is measured in different crops under\ndifferent management in different studies.”\nR2-4. Another limitation relating to yields concerns the land not in production where non-\ncrop diversification measures are used. This also needs to be acknowledged, e.g. that the\ntotal land area required to implement these methods needs to be larger so on a landscape\nscale, yields per hectare may not be as high.\nWe agree that there are diversification practices that require more land for implementation,\nsuch as embedded semi-natural/natural habitats or flower strips. We have mentioned this\naspect now in our interpretation and discussion of the results related to the benefits of non-\ncrop diversification. See L138-143:\n“It is noteworthy, however, that certain practices, such as embedded natural habitats or\nflower strips require more land and may limit the yield per unit area. Additionally, the\neffectiveness of the uses of organic amendments depends on factors such as the application\nrate of organic inputs (MacLaren et al., 2022; Yao et al., 2022). Therefore, reaping the long-\nterm benefits of these practices requires a nuanced consideration of these implementation\nconditions.”\neffects will be reported for rates of application that could not be scaled to the landscape\nlevel i.e. organic matter from a large area is being concentrated on a specific plot of land to\nachieve benefits, but this approach could not be scaled to the landscape. This also needs\ndiscussing.\nThanks for this suggestion. We do agree that the rate of application will determine organic\namendment benefits. We have, therefore, added this aspect to the interpretation of organic\namendment benefits (L140-143):\n“Additionally, the effectiveness of the uses of organic amendments depends on factors such as\nthe application rate of organic inputs (MacLaren et al., 2022; Yao et al., 2022). Therefore,\nreaping the long-term benefits of these practices requires a nuanced consideration of these\nimplementation conditions.”\nR2-6. The authors state that they couldn't find studies on climate regulation in organic\nsystems, but Gattinger et al have done reviews on this topic. Please check if these are\nincluded in your study.\nThis study is included in our dataset, which had effect sizes of carbon stocks and soil organic\nmatter (SOM), which are categorized under carbon sequestrations, soil fertility, and nutrient\ncycling, but not climate regulation.\nR2-7. The data for inoculants is weak and I suggest excluding this from the study. I am not\nconvinced that all appropriate studies were sourced. I also don't consider inoculants a\ndiversification strategy; they are an input substitution approach that is often applied within\nnon-diversified systems of production.\nThank you for this point. We agree that including inoculation as a diversification practice is\ndebateable. However, as inoculation is included in previous syntheses and we could source\nsome data, we prefer to present the limited long-term evidence to then highlight not only the\nshort- but also the long-term research gaps of this practice. In this sense, we hope that our\nwork can serve as a reference for future research in this direction.\nDetailed comments from the main manuscript:\nR2-8. Commented [A1]: Some of these keywords are in the title, therefore do not/should\nnot, be in the keywords. Choose synonyms for these words, not the same ones as in the\ntitle.\nWe have revised our keywords also following the recommendation of reviewer R3-12:\n15\n“Resilience, nature-positive, sustainable farming, crop yield, socioeconomic benefits,\nagroecology, long-term benefits.”\nR2-9. Commented [A2]: Might want to refer to regenerative agriculture and agroecology as\ntwo commonly used terms for farming systems that integrate these practices.\nWe have now included a link to agroecology and the link to agricultural diversification as the\nkey underlying principle. See L38-40:\n“Transforming the global food system requires nature-positive production systems (Hodson et\nal., 2021; von Braun et al., 2021) using agroecological farming methods based on agricultural\ndiversification practices to achieve sustainable food production (FAO, 2019; HLPE, 2020;\nWezel et al., 2020).”\nAlthough we agree that our work can be linked to regenerative agriculture, we also concur\nwith robust scientific literature that regenerative farming is one of the recent – somewhat\nconfusing terminologies that is encompassed within agroecology (see Bless et al., 2023). We,\ntherefore, decided to not mix both terminologies in our manuscript.\nR2-10. Commented [A3]: Throughout the paper the use of “pest control” to describe this\nresponse is confusing - when the detail on the responses used is reviewed, it is clear that\nthis response variable would be better termed “pest pressure”; declines in this variable\nindicate lower numbers of pests. A decline in “pest control” would imply less effective\ncontrol of pests! So, I advise changing the name of this variable throughout the paper to\navoid this confusion.\nCommented [A4R3]: Later in the paper you refer to the service of “pest control” which\nmakes sense; perhaps in the figure legend or text you can mention that a decline in this\nvariable implies an “increase/improvement” in the service.\nWe combine our reply to your previous two comment, because they both relate to the naming\nof the “pest control” category. Our pest control category includes different types of variables\nand – as mentioned also by the reviewer – refers indeed to the service. We, therefore, decided\nto keep the category name as “Pest control”, because we think that renaming the category\ninto ‘pest pressure’ would i) not reflect the composition of the category (sensu Supplementary\nTable 8) and ii) prevent us from referring to the pest control service. In our study, an increase\nin “pest control” refers to a service increase and, hence, desirable outcomes or benefit\nincreases. We inversed the signs of all effect sizes that are associated with non-desirable\noutcomes to unify the measure (see Supplementary Table 8 and our response to comment R1-\n3). We clarified that we are referring to the pest control service throughout the manuscript –\nsee for instance L208-216:\n“Our analysis also revealed a temporal increase of the lose-lose relationship between crop\nyield and pest control services (Fig. 3E). However, the effects of diversification on pest control\nservices were not statistically significant over time (Fig. 1, Supplementary Table 1-2), and the\nprobability of this outcome increased over the years (Supplementary Fig. 1). This indicates\nthat diversified farming may provide concomitant benefits to some pest control agents and\npests (Tscharntke et al., 2016), which leads to some negative effects on crop yield.”\nThank you for this comment on a lack of clarity. We meant ‘slowing biodiversity losses’ due to\nagriculture. However as this expression is not clear, we changed it into “[..] mitigating\nbiodiversity losses [..]”. See L96\nR2-12. Commented [A6]: Change vertical title to socioeconomic, not socioeconomy\nThanks for this suggestion. We have changed it to ‘Socioeconomic’.\nR2-13. Commented [A7]: Very effective graphics.\nThanks a lot for this positive comment.\nR2-14. Commented [A8-9]: For non-crop diversification and also intercropping, how was\nthe land removed from production taken into account for yields? Are these yields corrected\nfor total land use i.e. land equivalent ratios? Always more complex than the simple result -\norganic amendments have associated carbon footprints; non-scalable rates of application\nneed to be taken into consideration i.e. if they are applied at rates that result in depletion of\ncarbon elsewhere. This should be discussed in the paper.\nWe agree that these aspects are highly relevant when examining the effects of diversification\npractices. It is, however, critical to remember that our second order meta-analysis is the result\nof data extracted from meta-analyses of primary empirical studies. First, we trust that the\nauthors and peer-review process of the original empirical studies has ensured an unbiased\ncomparison between diversified and conventional practices. Second, we have applied rigorous\nstudy quality inclusion criteria to make the underlying database for our second order meta-\nanalysis as robust as possible for such a high-level methodological approach. And lastly, we\nhave discussed this aspect now in our interpretation and discussion of the results related to\nthe benefits of non-crop diversification (L138-143):\n“It is noteworthy, however, that certain practices, such as embedded natural habitats or\nflower strips require more land and may limit the yield per unit area. Additionally, the\neffectiveness of the uses of organic amendments depends on factors such as the application\nrate of organic inputs (MacLaren et al., 2022; Yao et al., 2022). Therefore, reaping the long-\nterm benefits of these practices requires a nuanced consideration of these implementation\nconditions.”\nthe organic amendments and potential to apply these approaches at a landscape scale.\nIn our paper, we define these practices as intentionally added; thus, we believe that the\nsources of the amendment are not very relevant at this level of syntheses. We have, however,\nmention the importance of the amount of amendment applied (see also our reply to comment\nR2-5; L138-143):\n“It is noteworthy, however, that certain practices, such as embedded natural habitats or\nflower strips require more land and may limit the yield per unit area. Additionally, the\neffectiveness of the uses of organic amendments depends on factors such as the application\nrate of organic inputs (MacLaren et al., 2022; Yao et al., 2022). Therefore, reaping the long-\nterm benefits of these practices requires a nuanced consideration of these implementation\nconditions.”\nR2-16. Commented [A11]: A weakness of comparing crop yields from diversified systems\nwith non-diversified is that studies usually report yields for a single year for the same crop,\nbut in diversified systems, that crop may not be grown as frequently; there may be phases\nof the production cycle where no food crops are grown, therefore, the impacts on food\nproduction may not be positive, even when yields in an individual year for a specific crop\ntend to be higher in diversified systems. This needs discussing.\nThanks for these comments. We understand that diversified farming systems are complex and\nthat these points are highly relevant for implementation. As we also mentioned in response to\nyour comment R2-3, in our second order meta-analysis, we include meta-analyses based on\nempirical studies that compare simplified with diversified practices (including rotations with\na fallow period etc.). These empirical studies would have accounted for such differences in\nyield measures in the first place. Overall, these differences in how yield is measured in\ndifferent crops under different management in different studies can be accounted for by\nincluding study ID as random effects in our second order meta-analysis. Now we have\nincluded this detail in our Data Analysis section (L389-395):\n“For each model, we first performed null model comparisons to determine the best random\neffects structure for each category of the response variable. For this, we compared the AIC of\nnull models with different random effects structures based on effect size IDs and study IDs (see\nSupplementary Table 11) to account for heterogeneity or between-study variations from the\nprimary studies and effect sizes (Schmidt & Oh, 2013). Specifically, study ID as a random effect\nwould, for instance, account for differences in how yield is measured in different crops under\ndifferent management in different studies.”\nR2-17. Commented [A12]: This is very much the approach of regenerative farming\nsystems and should be mentioned here.\nagroecology and regenerative agriculture are important approaches that are both relevant to\nmake the case for our work. We have included the term “agroecology” in the new keywords\n(also following your (R2-8) and Reviewer 3’s (R3-12) suggestion).\nWe have also included a link to agroecology in the Introduction (sensu R2-9) See L38-40:\n“Transforming the global food system requires nature-positive production systems (Hodson et\nal., 2021; von Braun et al., 2021) using agroecological farming methods based on agricultural\ndiversification practices to achieve sustainable food production (FAO, 2019; HLPE, 2020;\nWezel et al., 2020).”\nAlthough we agree that our work can be linked to regenerative agriculture, we also concur\nwith robust scientific literature that regenerative farming is one of the recent – somewhat\nconfusing terminologies that is encompassed within agroecology (see Bless et al., 2023). We,\ntherefore, decided to not mix both terminologies in our manuscript.\nR2-18. Commented [A13]: Probably yes, in a single year, but I am doubtful that total\nyields/food production is improved relative to high-input conventional agriculture. So need\nto discuss the shortcoming of any study that is considering yield of a specific crop in a given\nyear within a cropping system.\nWe agree that this is an important aspect to note. We have, hence included the following\nqualifier in the trade-off section (L195-197):\n“It is important to note, however, that this is necessarily highly aggregated information and\nthat these relationships over time will vary depending on crop type and diversification\npractice.”\nR2-19. Commented [A14]: There have definitely been metaanalyses on soil C stocks in\norganic systems and also on GHG emissions in organic vs conventional. See papers by\nGattinger et al.\nAs in response to your comment R2-6, we included Gattinger et al. 2012 in our dataset, which\nhad effect sizes of carbon stocks and soil organic matter (SOM), which are categorized under\ncarbon sequestrations, soil fertility, and nutrient cycling, but not climate regulation.\nR2-20. Commented [A15]: The dataset for inoculation in this study appears to be quite\nlimited; I am not convinced that the search terms located all relevant papers. I would be\ninclined to drop this category of diversification from the analysis.\ninoculation as a diversification practice is debateable. However, as inoculation is included in\nprevious syntheses and we could source some data, we prefer to present the limited long-term\nevidence to then highlight not only the short- but also the long-term research gaps of this\npractice. In this sense, we hope that our work can serve as a reference for future research in\nthis direction.\nR2-21. Commented [A16]: Where does this value come from?\nThis value describes the percentage difference between non-temporal effect sizes and effect\nsizes after different duration of practice application. The estimate was obtained by\ncalculating the percentage difference between the effect size value of non-temporal effects\nand the effects at 10 or 20 years. We have now provided a range of these estimates in\nSupplementary Table 2 for illustration.\nR2-22. Commented [A17]: First mention of agroecology. It might be good to explain earlier\nin the paper that the diversification practices you are referring to fall under the umbrella\nterm of agroecology or regenerative farming.\nThank you for this important point. We now have included a link to agroecology in the\nIntroduction (sensu R2-9) See L37-39:\n“Transforming the global food system requires nature-positive production systems (Hodson et\nal., 2021; von Braun et al., 2021) using agroecological farming methods based on agricultural\ndiversification practices to achieve sustainable food production (FAO, 2019; HLPE, 2020;\nWezel et al., 2020).”\nAlthough we agree that our work can be linked to regenerative agriculture, we also concur\nwith robust scientific literature that regenerative farming is one of the recent – somewhat\nconfusing terminologies that is encompassed within agroecology (see Bless et al., 2023). We,\ntherefore, decided to not mix both terminologies in our manuscript.\nR2-23. Commented [A18]: I am not sure that it does! The work has not addressed the\nd the\ndifferences between the systems. Particularly the rotational differences that mean the\nselection of crops grown and income streams may be quite different in a diversified system\ncompared to non-diversified.\nThank you for this comment. In our analysis, the diversification practice is always compared\nto a simplified system. As such, a comparison between systems more generally is what the\nintention of this manuscript is and we do think that this is possible with our approach. We\nagree that there are variations across different practices, and we believe that using study id\nas random effect will account for most of these variations. The high data aggregation level of\nour second order meta-analysis and diversity of practices did not allow us to dive into details,\nsuch as rotational differences. The goal of our work is a high level summary on the effects of\ndiversified systems compared to simplified systems on socioecological and environmental\nfactors over time with the most robust data currently available.\nuseful.\nWe have now included the definition of agricultural diversification in the method section\nL310-312:\n“In this study, we defined agricultural diversification or diversified farming systems as any\npractice that purposely incorporate functional diversity into farming systems 6.”\nR2-25. Commented [A20]: Very robust approach to selecting papers for the final analysis.\nWe thank this reviewer for this positive comment.\nR2-26. Commented [A21]: Defined how?\nSensu our reply to comment R1-9, we now have included an explanation of how we defined the\nquality of the studies. See Lines 376-384.\n“We evaluated each study's quality based on the methods used and the reported results (for\nmore details, see Supplementary Table 10), as these aspects determine the accuracy of the\nfirst-order meta-analysis results, which then affect the outcomes of second-order meta-\nanalyses models 67. More specifically, we assigned a score of 1 or 2 to each of 8 different\ninformation from the paper based on their absence or presence, respectively. For each paper,\nthe total score could vary between 8-16 and we considered a paper as low quality when the\nscore was from 8 to 13 and high quality when from 14 to 16 (see supplementary Table 10).”\nDetailed comments from Supplementary material:\nR2-27. Supplementary Table 1: Not clear how each group of terms was linked - with AND?\nthe diversification terms would not direct to research on diversification, e.g. crop*? why\nwould this lead to a paper on diversification?\nThe table provides a simple representation of the keywords we used to search for papers. We\nhave revised it now to show where the ‘AND’ is included into the research strings. We included\ncrop* in the research string to ensure that we included all meta-analyses related to crop\nproduction and in case a paper does not have a focus on any of the other keywords. This\nkeyword makes it certain that we included all papers that include any crop production system\nand the response variables that we are interested in.\nR2-28. Supplementary Table 2 NO2: Not normally reported in agricultural studies.\nstrings (Hu et al., 2022; Kumara et al., 2020; Wang et al., 2018), and decided to add it for\ncompleteness.\nR2-29. Supplementary Table 2 – soil NH5: what is this? should it be NH3? ammonia? I am\nnot sure how this is relevant to climate regulation; not a GHG.\nThank you for pointing this out. It was a typo, refers to soil NH4, and – because it is not a GHG,\nwe removed it from our analysis of the effects on climate regulation.\nR2-30. Supplementary Table 3 - Why no agroecology? regenerative? biodynamic? in this\nlist? Also, conservation agriculture?\nWe do agree that all these terms provide useful context. When designing literature search\nstrings, adhering to the parsimony principle is critical to strike the best balance between\nmanageable effort and search completeness. We, therefore, chose to focus on established\npractices that fit into the definitions of agricultural diversification (sensu Jones et al., 2023;\nTamburini et al., 2020). We also used snowballing on our search results (sensu R1-5), whereby\nidentified relevant studies are screened for additional missing studies, to ensure that all\nrelevant meta-analyses have been included in our analysis.\nR2-31. Supplementary Table 3 – EMF Is this EM? Effective microorganisms? I am not sure\nthe \"F\" is part of this acronym?\nThank you for pointing this out. EMF stands for ectomycorrhizal fungi. We have now included\nthe full term for this abbreviation, as well as for AMF, in Supplementary Table 9.\nR3-1. The manuscript “Decades matter: Agricultural diversification increases financial\nprofitability, biodiversity, and ecosystem services over time” quantifies long-term effects of\nagricultural diversification practices on ten social-ecological outcomes based on 184 meta-\nanalyses and 4,260 effect sizes, covering a time span of 50 years. The authors use\nhierarchical meta-regression models to estimate effects of diversification, and\nsubsequently a multinomial hierarchical model to estimate trade-offs associated with crop\nyield and ecosystem service outcomes.\nI commend the authors for the quality and innovativeness of the manuscript. Addressing\nthe temporal dimension of diversification effects on social-ecological outcomes is urgently\nneeded information for scientists, decision-makers, farmer, and the public alike. Before I\ncan recommend the manuscript for publication, I would kindly ask the authors to address\nmy comments/concerns detailed below.\nWe thank the reviewer for these encouraging comments. We have adjusted the manuscript\nbased on the feedback we received. Please find below our responses to the points and concerns\nraised.\nMAJOR\nR3-2. I like the set-up of the analysis with first establishing a baseline and then assessing\nthe differences to models with a time moderator. I also appreciate the many\nsensitivity/robustness checks that have been conducted. However, I have a few questions\nin that regard:\nYou have assessed publication bias, yet I was wondering about geographic biases as certain\nregions might covered by more meta-analyses compared to others. Moreover, the potential\nselection bias of which farms participated in the original studies in diversified practices\n(random, partial random, or opportunistic sampling) remains unclear.\nThanks for these comments. In general, we agree that geographic and farm selection bias are\nan important source of variation and we have considered this in the analyses of our work. Our\nstudy is based on second-order meta-analysis and as such it summarises the results of many\nindividual first-order meta-analysis. Most of the meta-analyses included in our manuscript\nhave a global (98), continental, or sub-continental scale focus (15) and comprise now over\n4,600 effect sizes. Country specific meta-analyses came from China (33), Australia (1) and\nBrazil (1). As more than 4600 effect sizes could not be allocated to specific locations, we could\nnot run robust country specific analyses. Farm selection bias is extremely difficult (if not\nimpossible) to address explicitly with second order meta-analyses, because this requires data\nfrom the original papers included in the individual meta-analyses. This effort is not feasible\nfor the purposes of our study. Consequently, we used the statistical model structure to\nimplicitly account for this type of variation by including study ID as a random effect.\nnot occur in vacuum, but in landscapes and social contexts. Hence, I can imagine that\neffects, especially long-term effects, might differ in different regions. Would it be possible to\ntest if, for example, landscape composition moderates diversification outcomes?\nThis is indeed a very exciting angle. However, as mentioned in reply to your comment R3-1,\nour work is a synthesis of meta-analyses that already had synthesised information from\nempirical studies. It was therefore impossible for us to gather information related to\nlandscape compositions. In fact, none of the individual meta-analysis studies included in our\nwork contains landscape diversification over time as part of the practices. We, therefore,\ncould not include this information in our studies and dataset.\nR3-4. I appreciate the efforts to exclude meta-analyses with similar underlying data, yet (to\nme knowledge) meta-regressions still suffer from the problem of multiple testing (Type 1\nerror). Hence, correcting for this, e.g., by using Bonferroni methods, would be advisable.\nThank you for pointing this out. Bonferroni corrections are necessary when multiple tests are\nconducted simultaneously. In our analysis, we either predicted the non-temporal or temporal\neffects of diversification, but we did not do any comparisons between levels, which is prone to\nerrors associated with multiple comparisons. Furthermore, to reduce the bias in our\nprediction, we used the precision of the original effects and the number of original\ncomparisons as weights in our meta-regression model. This approach allowed us to account\nfor precision and give more weight to studies that had larger sample sizes. Therefore, we do\nnot require Bonferroni methods in our analyses to correct for Type 1 errors.\nR3-5. The temporal depth of the study is remarkable, but this also creates some problems.\n1.1 How did the authors control for potentially changing social-ecological conditions over\ntime (e.g., climate change)? The effect sizes inherently include such changes, which makes\nit difficult to distil the pure effect of the practice/strategy.\nThanks for raising these points. We agree that climate change might influence the effects of\nagricultural diversification ( e.g. Vicente-Vicente et al., 2016), but we could not find enough\nstudies with effect sizes that treat both duration and climate variables. Consequently, we\ncould not include this factor as part of the predictors.\nR3-6. Several outcomes have only few data points for long-term effects (e.g., beyond 20\nyears). Although this is reflected in the wide CIs, this arguably affects the direction of the\neffect. A good example is pollination, for which 3 data points define the direction of the\neffect. Without those data points, the effect would be neutral (or even negative).\nmodel selection to test whether there is a non-linear relationship between duration and the\ndifferent categories of response variables. For this, we first looked for the best random effect\nstructure by comparing the AIC of the null models, and we performed another model selection\nto determine the nature of the relationship between the effect sizes and the duration, again\ncomparing the AIC of 4 different models with duration as 1) linear, 2)quadratic, 3) cubic, and\n4) quartic terms, using random effects of the best models from the first model selection. We\nthen added the results in Supplementary Table 11, which showed that only pollination and\npest control had a non-linear relationship with the duration of practice. We have added these\ndetails in the Methods section see L403-407.\n“In addition, we performed another model selection based on AIC to determine whether the\nrelationship between our response variable and durations of practice follows a linear,\nquadratic, cubic, or quartic relationship (see Supplementary Table 11), using the best random\neffects selected from null model selection.”\nFor pollination, we found a non-linear relationship between the effect sizes and duration of\ndiversification practices in our new analysis. Our model result showed that, although the first\ncurve of the quadratic model is not significant, we still detected a positive effect size with a\nslight increase up to 10 years. Our prediction showed that pollination services can increase up\nto 163% after 5 years compared to the first 3 years of applying diversification practices. Thus,\neven without the three data points above 10 years, we still have an increase in benefits, which\nis overall positive during the first 10 years.\nR3-7. Although I understand the motivation for calculating trade-offs and synergies\nbetween ecosystem services and crop yields, I think this introduces a clear argumentative\ndirection in the paper (profits vs. conservation). Why did the authors refrain from\nassessing joint effect strengths of diversification practices/strategies on all paired\noutcomes (as there might be trade-offs between certain outcomes that currently go\nunnoticed and unreported)?\nThank you for this suggestion, which would have been indeed very useful and would have\nmade our arguments for implementing certain practices much stronger. Unfortunately, we\ndid not have enough data to run the trade-offs analyses at practice category level as we only\nhad around 2800 effect sizes for the trade-off analyses for yield vs. ecosystem services over\ntime compared to a total 6100 effect sizes for the second-order meta-analyses regressions. We\nnow mention this in our research gap section (L237-239):\n“More studies are needed that concomitantly focus on diversification effects on yield or other\nservices to enable analyses of multiple and individual diversification practices over time.”\nR3-8. I was wondering whether the two social-economic factors are sufficiently different\nfrom each other, as I would assume that yields and financial profitability are quite strongly\nrelated in many world regions. Alternative examples would be human well-being and/or\nfood security, and I suggest including such more human-focused outcomes in the analysis,\nif this is possible based on the meta-analyses used.\nThank you for these suggestions. We agree that yield and profitability can be related but don’t\nhave to be depending on the specific focus variable. For instance, depending on geography\nand systems, gross income can be largely driven by yield, or largely by subsidies and input\ncosts but not yield. Our effect sizes also include parameters independent of crop yield such as\nproduction or input costs. In fact, our trade-off analysis between crop yield and financial\nprofitability showed a trend of win-win outcomes during the first 7 years and which lose-win\noutcomes become more dominant, meaning that despite loss of yield, farmers could still make\nprofit by implementing diversification practices on their farms.\nWe agree and would find it interesting to include other variables related to human-well-\nbeing. However, we feel that adding financial profitability has already covered a critical and\nprimary dimension for farmers of with agriculture is their main livelihood strategy. We feel\nthat additional human-wellbeing related outcomes and the necessary literature searches are\nunfortunately outside of the manageable effort at this stage of the manuscript, especially\ngiven the extensive re-analyses of our intensively updated dataset.\nR3-9. Your literature search followed the PRISMA review protocol, and I suggest to clearly\nstate that in the methods section.\nThanks for this suggestion. We have added this sentence to the Methods section (L301).\nR3-10. From what I understand from the methods section, the authors did not assess the\neffect of combined practices on social-ecological outcomes. Was there a reason for this, as\nthis would have been quite interesting?\nWe completely agree that it would have been very interesting to look into the combined\neffects of different practices over time on socio-ecological outcomes, as it had been recently\nfound in a non-temporal and empirical study by Rasmussen et al.,( 2024). However, we did not\nfind sufficient meta-analysis studies that investigated this type of combined effects. In fact,\nonly one meta-analysis by (Jones et al. ( 2021), which is also included in our study as part of\norganic farming, included duration and combined practices.\nR3-11. The abstract should not contain references.\nWe have removed the references from the Abstract.\nyou might want to consider using the term Nature’s Contribution to People, as suggested by\nthe IPBES in their most recent report. Perhaps also consider adding terms such as\nresilience.\nThanks for these suggestions. We now use the following keywords:\n“Resilience, nature-positive, sustainable farming, crop yield, socioeconomic benefits,\nagroecology, long-term benefits.”\nDETAILS\nR3-13. Line 20-21: I suggest rephrasing: ‘Many studies have confirmed agricultural\ndiversification with practices such as intercropping, organic farming, or soil inoculations,\nas a suitable pathway…’.\nWe thank this reviewer for this suggestion and changed the sentence accordingly.\nR3-14. Line 23: Which method did you apply to quantify the effects? I assume some sort of\nmeta regression?\nThank you for this question. We now specify this in the manuscript (L20-23):\n“Here, we used a second-order meta-analysis to quantify [..]”\nR3-15. Lines 25-28: It is unclear which diversification practice(s) this sentence refers to.\nAny practice, a specific practice, or a certain combination of practices? Please clarify.\nWe now clarified this in L23-26:\n“While the overall diversification practices increased financial profitability, biodiversity,\npollination, all aspects of soil quality, and carbon sequestration by 37 to 189% over 20 years,\nwe found no significant effects on crop yield, pest control, and climate regulation.”\nR3-16. Line 27 & 29: Increases of 2800% and 2000%, respectively seem extremely high.\nI do not doubt these values, but I assume that they are the result of the small number\nproblem with respect to relative increase of very small values over time. Perhaps report\nthe effect sizes as well?\nThanks for this helpful comment. As mentioned also in reply to comment R1-12, we re-checked\nour analyses, dataset and the presentation of the revised results. We have revised the results\nin the Abstract as follows (L23-26):\n27\n“Diversification increased financial profitability, biodiversity, pollination, soil quality, and\ncarbon sequestration from 37 to 189% over 20 years, and we found no significant effects on\ncrop yield, pest control, and climate regulation.”\nR3-17. Lines 28-29: are those the same benefits as in the previous sentences? Do all of\nthem show a positive response to practices?\nThank you for this comment. We have refined this section in the Abstract (L23-27):\n“Diversification increased financial profitability, biodiversity, pollination, soil quality, and\ncarbon sequestration from 37 to 189% over 20 years, and we found no significant effects on\ncrop yield, pest control, and climate regulation. Non-crop and crop diversification practices\nand use of organic amendments increased or maintained most of these benefits over time.”\nR3-18. Line 30: Which other services? This is not defined and needs an explanation. Also,\nwhat happened after those 25 years? Did yields go down?\nThank you for asking this clarifying comment. Here, we mean yield vs. all services combined.\nThus, we have now changed the word “other” to “all” as shown in Fig. 3.\nAfter revising our dataset, our analysis now suggests win-win outcomes up to 40 years and\nsubsequently the probability is highest for losing yield while having ecological. However, this\nresult needs to be interpreted in perspective of our systematic review, which showed that\ndespite these negative effect sizes, the difference from conventional farming is not statistically\nsignificant. This suggests that with time yield stabilises rather than declines (See\nSupplementary Fig. 1).\nR3-19. Line 33: I am not sure if I agree with the statement that the results actually provide\nevidence for allowing diversification upscaling, as this was not analysed (they certainly\nencourage efforts to upscale, though).\nThanks for these comments. We have rephrased the sentences to better reflect our results\n(L29-33):\n“Our synthesis provides the urgently needed evidence for farmers and other decision-makers\nthat diversification increases long-term financial profitability, biodiversity, soil quality, and\nclimate change mitigation benefits. Therefore, agricultural diversification can be upscaled for\nlong-term socioeconomic and nature-positive outcomes and, ultimately, a global food system\ntransformation.”\n(dx.doi.org/10.1126/science.adj1914), which provides evidence of joint environmental and\nsocial benefits from diversified agriculture.\nThank you for alerting us of this important reference. We now cite this reference for instance\nin the discussion related to the importance of combined practices.\nR3-21. Line 64: Do you mean food security? Otherwise, reformulate into “addressing food\ninsecurity”?\nThanks for noticing this. Yes, we meant food security.\nR3-22. Line 73: Please provide more details of what mean with ‘long-term effects’, e.g. the\nminimum number of years between assessments.\nWe mean the effects of the duration for which a diversification practice is applied onto\necosystem services/yield. We now have rephrased this sentence to make it more clear (L73-\n74):\n“[..] we quantified the influence of duration (years) of diversification practices on their effects\non socioeconomic factors [..]”\nR3-23. Line 75: Do you mean non-agricultural biodiversity?\nYes, and to simplify the term we used “biodiversity”, which does not include agrobiodiversity\nin this paper. We understand that this term is broad and can include many aspects depending\non the field. Therefore, we now provide more details of all variables that are contained in this\nterm in the Supplementary Table 8.\nR3-24. Lines 75-77: Do the meta-analyses contain information about reduced\nenvironmental externalities? If yes, this would be interesting to include as well.\nWe are not sure that we understand the comment correctly. To our knowledge, all the\nresponse variables related to ecological benefits are direct measures of the reduction of\nenvironmental externalities in crop production: biodiversity loss, water depletion, soil\ndegradation, greenhouse gas emission, etc. As such, we think that we have addressed this\ncomment already, but we may have misunderstood the reviewer here.\nR3-25. Lines 84-85: Well, it did not reduce yields on average…it surely does in some meta-\nanalyses.\nThank you for this important comment. We now change the wording of the sentence to (L83-\n85):\n29\n“The analysis of socioeconomic factors showed that while agricultural diversification did not\nsignificantly change crop yield over time, [..]”.\nR3-26. Lines 87-88: This statement hinges on the conditions that stabilizing yields and\nincreased profits occur in the same regions/on the same farm. As you report average effect,\nI would caution this.\nThanks for pointing this out. We agree that here, the interpretations are based on the average\nfrom different studies from different regions. First, we reported the aggregated patterns of the\nsecond-order meta-regression analysis. In the subsequent trade-off analysis, a win-win\nrelationship between yield and financial profitability turned into a lose yield-win finance\nrelationship after 7 years. However, these lose yield relationships are not statistically\nsignificant (see Supplementary figure 1) compared to conventional agriculture (L185-192):\n“Our analyses also showed win-win situations with financial profitability, soil fertility,\nnutrient cycling, and carbon sequestration for 7, 45, 47and 25 years, respectively (Fig. 3) and\nlose yield-win ecosystem service situations becoming more common afterwards, interestingly\nincluding lose yield-win financial profitability. Our results indicate that compared to\nmonocultures and conventional agriculture, agricultural diversification can improve crop\nyield in short terms while increasing farmers’ income, improving soil nutrients, and capturing\nand storing carbon from the atmosphere in the long run, thus, achieving nature-positive\nsystems and equitable livelihoods for the farmers 1,49,50.”\nTo get these results we ran trade-off analyses within the individual study and not across, to\nmake sure that at least the analysis is done with the data from at least the same region if not\nfrom the same location or farm, which we specified in L442-445:\n“We analysed the variation over time in the trade-offs associated with crop yield. We selected\nstudies that tested diversification effects on crop yield and other services simultaneously and\ncategorised each effect size as respective, win, or lose if the effect size was positive or negative,\nregardless of their statistical significance.”\nBesides this, we also added that the outcomes of the trade-off analysis might still vary\ndepending on the locations among other factors in L194-196:\n“It is important to note, however, that this is highly aggregated information and that these\nrelationships over time will vary depending on location, crop type and diversification\npractice.”\nR3-27. Lines 104-105: What about transitions that are not at the extremes of the gradient?\nPlease see the paper by Beckmann et al. 2019 (https://doi.org/10.1111/gcb.14606).\nThank you for this comment and the reference that is a nice illustration of how the yield-\nbiodiversity relationship is shaped by crop type and the response of a species group. Indeed,\n30\nthere are many factors that will shape outcomes when diversification practices are\nimplemented. As mentioned in response to previous comments, our second-order meta-\nanalysis, we are limited to a high-level research question and, hence, the identified meta-\nanalyses included. Understanding these details that will undoubtedly shape outcomes requires\ntargeted meta-analysis such as the one by Beckmann et al. 2019 and local empirical studies\nfor instance on simultaneously testing social and environmental variable changes in response\nto diversification practice implementation.\nR3-28. Line 107: ‘More sustainable’ or ‘less environmentally impactful’.\nThanks for this suggestion. We have changed it to “more sustainable” (L116).\nR3-29. Lines 174-177: Why is that? Why do yields lose after time when ecosystem services\nwin?\nA yield-lose – ecosystem service-win outcome will be mediated by the diversification practice.\nFor instance, agroforestry as a non-crop diversification strategy is well known to reduce\nyields but benefit carbon sequestration. Flower strips may reduce yields from reduced\nproduction area, which can have various effects on pest control agents. We have now included\na possible explanation for all the trade-off outcomes (see for example L209-217):\n“Our analysis also revealed a temporal increase of the lose-lose relationship between crop\nyield and pest control services (Fig. 3E). However, the effects of diversification on pest control\nservices were not statistically significant over time (Fig. 1, Supplementary Table 1-2), and the\nprobability of this outcome increased over the years (Supplementary Fig. 1). This indicates\nthat diversified farming may provide concomitant benefits to some pest control agents and\npests 54, which leads to some negative effects on crop yield. Use of agricultural technologies\ncould help to reduce such undesirable effects of agricultural diversification via the use of pest\nresistant varieties or more effective pest control technologies 55.\nR3-30. Line 225: Perhaps link to LTSER research here?\nThanks for this suggestion. We added this in our discussion as follows (L264-267):\n“Such funding mechanisms will allow researchers to establish an integrated research network\nand databases such as LTSER platforms 60 on a global scale, which will then promote open\nresearch, facilitating syntheses in social and ecological research 61.”\nR3-31. Line 237: This is not entirely true…on average it does not result in yield declines.\nWe have rephrased the sentence to (L270-273):\n31\n“While biodiversity benefits increase over time, there is a non-significant decline in crop yield\ndespite the commonly expected trade-off associated with biodiversity integration in\nfarmlands.”\nR3-32. Line 241-242: Well, if awareness is increasing in NA and Europe, shouldn’t these\nalso be the regions where efforts should be enhanced (notwithstanding the fact that those\nregions are largely characterised by industrialised and specialised agriculture)?\nTrue. However, we also wanted to emphasise the adoption aspect, which is still lower in some\nregions. Thus, we added the word ‘adoption’ to make this more explicit (L275).\nR3-33. Line 293: Do you assume not significant changes as ‘neutral’? To me, there is a\ndifference between both categories, and I would suggest to re-label the ‘neutral’ class to\n‘not significant’.\nThanks for this suggestion. We have changed it to ‘non-significant’.\nR3-34. Line 302: Your example would be the mean duration, correct?\nThank you for pointing this out. We have changed this to ‘mean duration’ (L356).\nR3-35. Line 303: Why two years and not one year (or five years)? The choice seems\narbitrary and needs at least one justification, ideally a sensitivity analysis, to quantify the\ninfluence of this choice on the results.\nThank you for this point. Unfortunately, we could not find (but would appreciate a pointer\ntowards) any reference on what number of years to add when an uncertain duration of effect\nsizes is provided in the reference study. Thus, adding two years was an educated guess.\nWe have now specified this in the manuscript in the methods section (L357-360):\n“When the duration was indicated as “>x” we added two years, as an educated guess since we\ndid not find any suitable guidance from the literature, so >10 years would become 12 years to\nhave a single duration for the corresponding effect size.”\nHowever, we have now run a sensitivity analysis predicting the effect of diversification\npractices with effect sizes with precise duration only and then compared the results to the\noutcomes from the full dataset (see Supplementary Fig. 9 below), showing overall no\nsignificant differences. We also added these details in the Data analysis section (L434-437):\n“We also performed a sensitivity analysis to determine whether including studies that report\nimprecise duration could change the outcome of our results. For this, we ran a separate model\n32\nwith only effect sizes that had exact duration. When compared to the results of the full\ndataset, we found no significant differences between outcomes.”\nSupplementary Fig. 3| Sensitivity analysis for the second order meta-analysis: comparison\nof model results from the effect of diversification over time using the i) full dataset and the\nii) dataset with precise duration only.\nWe changed it (L368). Thanks for this suggestion.\nR3-37. Line 313: … independence analysis for our 192 studies…\nWe have implemented this suggestion (L369).\nR3-38. Line 325: What do you mean with ‘overall’? Is this the total number of\ndiversification strategies and practices? Or is this reflecting if studies cover any practice?\nPlease clarify.\nHere, we mean all practices. The sentence now reads (L386-388):\n“We synthesized the effects of agricultural diversification on different categories of response\nvariables in all agricultural diversification practices combined and for each practice\nindividually.”\nR3-39. 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Nitrous Oxide Emission and Grain Yield in Chinese Winter\nWheat–Summer Maize Rotation: A Meta-Analysis. Agronomy, 12(10), 2305.\nhttps://doi.org/10.3390/agronomy12102305\nWe greatly appreciate the reviewers' feedback and have carefully addressed all points raised.\nThe outcome of this revision is certainly a stronger and more robust manuscript.\nSpecifically, we have added a discussion on the potential limitations of upscaling\ndiversification related to yield for crop rotation. In addition, we now discuss the quantities of\norganic amendments required in large-scale farming systems. Lastly, we have added a map\nto illustrate geographical bias and data gaps of the available meta-analyses studies included\nin our synthesis.\nBelow, we provide a point-by-point reply underneath all comments in blue.\nWe hope that you can consider the resubmission of our fully revised manuscript.\nR1.1 This manuscript investigates the long-term effects of agricultural diversification practices\non socioeconomic and environmental outcomes using a second-order meta-analysis. The\nauthors provide a compelling case for the benefits of diversification, particularly in financial\nprofitability, biodiversity, and pollination, supported by quantitative analysis. The focus on\ntemporal trends is an important contribution to understanding the sustainability of these\npractices.\nThe authors’ responses to my previous comments are satisfactory, and I appreciate the\nclarifications provided. My review is therefore divided into two sections: first, comments on\nthe revised manuscript for the next version, and second, feedback on the authors' responses\nto my previous requests.\nWe thank this reviewer for these positive comments. We are delighted that our responses\nand revisions have addressed the previous concerns.\nR1.2 \"Financial profitability increased significantly by 189% over 20 years compared to short-\nterm effects (< 3 years, Supplementary Fig. 2, Supplementary Table 2).\"\nIs it correct to interpret this as meaning that the effect is approximately doubled between 3\nyears and 20+ years? To avoid confusing the reader, I suggest specifying the value at 3\nyears.\nYes, that is correct. We chose to use percentages to make it more accurate. As suggested,\nwe have now added the mean values of the effect sizes (LnRR) for both < 3 years and 20\nyears in brackets to avoid confusion, as follows:\n“The analysis of socioeconomic factors showed that while agricultural diversification did not\nsignificantly change crop yield over time (Fig. 1), financial profitability increased significantly\nby 189% over 20 years (LnRR=0.3633) compared to short-term effects (< 3 years, LnRR =\n0.1259, Supplementary Fig. 2, Supplementary Table 2).” See L87-91.\nR1.3 \"Our analyses also showed that the probability of non-significant effects of agricultural\ndiversification on crop yield increased over time.\"\nCould this be phrased more clearly and simply for the reader?\nThanks for pointing this out. We have now rephrased the sentence for better clarity as\nfollows:\n“Our analyses also showed that, over time, the probability of agricultural diversification having\nno effect on crop yield increased (Supplementary Fig. 1)”. See L91-93.\nR1.4 \"These findings show that the crop yield of diversified farming systems stabilizes over\ntime.\"\nDoes your analysis fully support this statement? While an increased probability of non-\nsignificant effects may suggest that yields of diversified and non-diversified systems converge\nover time, this could also be due to greater heterogeneity or increased variability in study\noutcomes. The figure on crop yields (Fig. 1) does not seem to show a reduction in variance\nover time, does it?\nThank you for your thoughtful comment. Upon review, we agree that the statement regarding\nyield stability may not be fully supported by the analysis. While there is an increased\nprobability of non-significant effects, which could suggest that yields of diversified and non-\ndiversified systems may converge over time, this could also be attributed to greater\nheterogeneity or increased variability in the study outcomes. Additionally, as you pointed out,\nthe results on crop yields (Fig. 1) does not show a reduction in variance over time. Therefore,\nwe have rephrased the sentence to better reflect the findings as follows:\n\"These findings show that, while farmers may not experience significant benefits in terms of\ncrop yield from diversified farming systems over time, their income improves, highlighting the\nsocioeconomic viability of agricultural diversification in the long run 3.\" See L93-96.\nR1.5 For the crop yield data, I am still uncertain about the unique data point at 100 years.\nAlthough it might not affect the overall results, did you test the influence of this point?\nThank you for raising this concern. We did a test to detect, but we did not find outlier effects\n(see supplementary Fig. 7). We now also ran an analysis excluding this data point to assess\nits impact and there were no significant differences in the outcomes when this point was\nremoved (see figure 1 below). We decided to display all available data points, including the\none at 100 years, to highlight the gaps in terms of duration and to maintain consistency with\nthe other response variable categories. This approach ensures a comprehensive\nrepresentation of the data.\nFigure 1. Crop yield results with all available durations and only with durations up to 50 years.\nR1.6 For biological communities:\n3\n\"Agricultural diversification significantly increased biodiversity and pollination benefits by 64%\nand 629%, respectively, over 20 years (1093 years for pollination; Fig. 1, Supplementary\nTable 2).\"\nThe phrasing could be improved for clarity regarding the duration.\nThanks for this comment. We now rephrased the sentence as follows:\n“For biological communities, agricultural diversification significantly increased biodiversity by\n64% over a 20-year period and pollination benefits by 629% over 10 years (Fig. 1,\nSupplementary Table 2).” See L97-99.\nR1.7 Soil quality and climate change mitigation effects:\n\"All effect sizes of soil quality and climate change mitigation, except for climate regulation,\nwere significantly positive.\"\nThe formulation could be adjusted to clarify that it refers to the mean effect sizes being\npositive, not all individual effect sizes across the meta-analyses.\nThanks for pointing this out. We now rephrased it to highlight that the mean effect sizes and\nnot the individual effect sizes are positive:\n“The mean effect sizes of soil quality and climate change mitigation, except for climate\nregulation, were significantly positive. Soil fertility, nutrient cycling and carbon sequestration\nhad significant increases over time for (Fig.1)” See L113-115.\nR1.8 Crop diversification effects on pest control services:\n\"Crop diversification effects on pest control services did not change significantly over time,\nand there was no significant difference compared to conventional agriculture for up to 50\nyears.\"\nThis seems somewhat contradictory to other second-order meta-analyses, such as those in\nScience and Global Change Biology.\nThanks for pointing this out. Yes, the effect sizes from non-temporal models are different\nfrom the results of other second-order meta-analyses (we assumed that you referred to\nBeillouin et al., 2021 and Tamburini et al., 2020). We compared the datasets used in Beillouin\net al. (2021) and Tamburini et al. (2020) to ours and found that the majority of the meta-\nanalyses included in these syntheses were excluded during our screening due to missing\ninformation on practice duration or not using RR or LnRR as effect size measurements.\nSpecifically, for the paper from Global Change Biology (Beillouin et al., 2021), only 4 out of 14\nstudies on pest control were included in our dataset. These 4 studies account for just 8 effect\nsizes out of the 90 for pest control benefits from crop diversification practices in our dataset.\nThe remaining effect sizes were extracted from syntheses published after the Science\nAdvances or GCB papers, which reported more negative effects of crop diversification on\npest control (see, for example, Jones et al., 2023). For Tamburini et al. (2020), only 2 out of 7\nstudies on crop diversification and pest control effects were included in ours. These\ndifferences in study sets and datasets, along with the additional reported negative effects of\ndiversification on pest control in newer studies, explain the discrepancy between Beillouin et\nal. (2021), Tamburini et al. (2020), and our work.\nR1.9 R1-1:\nTamburini et al. is not the only second-order meta-analysis offering this type of classification.\nThanks for this feedback. We understand that Tamburini et al., 2020 is not the only synthesis\nthat offers a classification of agricultural diversification practices, but we opted for Tamburini's\nclassification because, at the time we designed and extracted the data for this study, it was\nthe only synthesis that also covered the majority of socio-economic variable categories we\nwere interested in. We then added financial profitability variable categories on top of\nresponse variable types in Tamburini’s. Our main approach was to build on existing second-\norder meta-analysis and incorporate temporal factors on top.\nR1.10 Supplementary Table 8:\no There appear to be some errors in the variables highlighted. For example, SOC is present\nin multiple lines but not highlighted.\nThat's correct, we forgot to highlight it for nutrient cycling but SOC is not a repeated response\nfor soil fertility and is therefore not highlighted in the corresponding line. Now we have\nhighlighted it for all other categories. Thank you for pointing this out.\nR1.11 Also, a minor clarification: Is it indeed beneficial to increase or decrease pH for soil\nfertility?\nIt depends on the focus crop. Nevertheless, we did not reverse the pH because it was not\npossible to identify the details of the crop behind each effect size. We decided that it is\npreferable to have an increase in pH from diversification practices due to the other effects of\nsoil acidification from chemical fertilizers on plants, soil organisms, and biogeochemical\nprocesses (Singh et al., 2020). While it would have been preferable to link the effect sizes to\ncrop preferences and the initial pH before the onset of a diversification practice, this\ninformation was not available for all the effect sizes. We have now run a separate analysis of\nthe effects of diversification on soil fertility without soil pH, and the results (see Figure 2)\nshowed no significant difference compared to the analysis that included soil pH.\nR1.12 R1.2: Your response addressing ambiguous variable categorization is appreciated.\nThe inclusion of sensitivity analyses, where variables are grouped in all potential categories,\nadds robustness to the methodology.\nThanks for this positive comment.\nR1.13 R1.3: The clarification regarding the NH5 typo and its removal from the climate\nregulation category is noted and appreciated. The explanation about reversing the sense of\neffect sizes for undesirable outcomes, such as production costs, adds clarity. Highlighting the\nvariables with this reversal in Supplementary Table 8 is helpful for transparency.\nThank you.\nR1.14 R1.4: No further comments, this is acceptable.\nThanks for this comment.\nR1.15 R1.5: Perfect.\nVery much appreciated.\nR1.16 R1.6: This is fine, but what about kappa or consistency in the extraction procedure?\nThanks for this clarification. The screening and data extraction were performed by one\nperson, so it is not possible to compute the Kappa for the screening decisions. Regarding the\nconsistency in the extraction procedure, it was conducted using the detailed guide described\nin our method, along with similar tools, depending on how the information was presented in\nthe meta-analysis.\nprovide access to them?\nFollowing this link: https://osf.io/zpr6a/?view_only=141e9cce92624c82b134621cfd51811c\nyou can find the dataset with anonymized studies and effects and the code we used to\nanalyse the data. We share these files privately as we are currently using the dataset for\nother research works.\nR1.18 R1.10: Well done, this must have been a significant amount of work. You have\nrevisited all the primary studies. If you had access to the databases from the meta-analyses\nand completed them, wouldn't you now have all the data needed for a first-order meta-analysis?\nThank you for this comment. Unfortunately, we did not have access to the primary data of all\nthe meta-analyses, which made it difficult to conduct a first-order meta-analysis. Additionally,\nthis approach would have required a different protocol and research strings.\nR1.19 . R1.12: OK, thank you.\nYou are very welcome.\nR1.20 R1.13: Perfect, thank you.\nWe are very happy to read this positive comment. Thank you.\nR2.1 This study is impressive and could have a big impact, so it is extremely important that\nthe limitations of agricultural diversification approaches are acknowledged and openly\ndiscussed.\nWe are very happy to read this positive comment about our study.\nR2.2 Currently, the concluding statement in the abstract is too one-sided, suggesting that\n“agricultural diversification can be upscaled for long-term socioeconomic and nature-positive\noutcomes and, ultimately, a global food system transformation.”\nThis statement does not acknowledge two key limitations: I referred to these in several places\nin my comments but I am not satisfied with the responses provided.\nThanks for this remark. We have now taken it into consideration and added a sentence about\nthe limitation before the last sentence of the abstract, connecting it as a prerequisite for the\nscalability of diversification practices as follows:\n“A large-scale implementation of agricultural diversification requires, however, careful\nconsideration of factors that mediate these benefits. Then, agricultural diversification can be\nupscaled for long-term socioeconomic and nature-positive outcomes and, ultimately, for a\nglobal food system transformation.” See L33-37.\nR2.3 First of all, studies that report crop yields in diversified versus more conventional\nsystems, normally compare yields for a single species in a single year. There is not normally\nany attempt to account for the frequency that each crop appears in the rotation. The authors\nrepeatedly refer to their statistical approach e.g. using study ID as a random effect\nsuggesting that this will address this problem. They also suggest that the authors of the\nstudies they use will have accounted for the “differences in yield measures in the first place”,\nbut this is not normally done. The authors seem to have missed my point here and I hope\nthey can address this really important limitation of the dataset and also the limitation of\nagricultural diversification approaches: diversified systems normally result in a lower\nfrequency of food crops in rotation so that even where yields for a specific food crop appear\nto be similar for diversified and intensive rotations (because comparisons are normally made\nfor a single year), at a rotational scale, total calories produced is often lower. This means that\nat a food system level, some moderation of diets is usually required when production systems\nusing “agricultural diversification” are modelled to achieve adequate nutrition for a society.\nThis needs discussing and the conclusions of the study need moderating to acknowledge this\nconcern.\nWe thank the reviewer for this detailed explanation. We have now integrated them into our\ndiscussion about crop diversification practices as follows:\n“Crop diversification can, hence, improve farmers’ income over time 44 and ensure the long-\nterm maintenance of crop yield 45. It is worth noting that, although crop diversification\npractices do not necessarily increase crop yield compared to conventional practices, they can\ncontribute to food security through diet diversification and might help improve resistance to\n8\nexternal shocks and climate change adaptation 46–48. These benefits may be limited by the\nnon-productive years in case of temporal crop diversification and by land availability for\nspatial crop diversification 48.” See L157-163.\n“It also enables broader landscape finance strategies, as our results provide detailed\nevidence of the anticipated trade-offs of agricultural diversification implementation and the\nassociated risks 69. However, implementing diversification also requires careful consideration\nof the factors that mediate individual diversification practices beyond the binary comparison\nbetween monocultures and diversified systems, including geographic variations across\nscales, and climate change, all of which are critical to ensure the benefits of agricultural\ndiversification practices 71–74. Thus, agricultural diversification provides further opportunities\nto shift towards a truly integrated strategy 33, whereby local culture, context and practices are\naccounted for, and ecosystem services are fully utilized for diverse livelihoods beyond crop-\nrelated productions 75. See L320-329.\nR2.4 Secondly, there is no acknowledgement that most studies reporting positive effects from\napplications of organic amendments are not “scalable” i.e. the rates of amendment applied\nare in excess of the quantity of material that could be produced from that area of land. This\nmeans that there simply is not enough organic material available to supplement the land at\nthese rates, which limits the potential of this practice unless sources of organic matter\nexternal to the farming system can be sourced. This issue is also not adequately addressed\nbelow. Both of these factors mean that agricultural diversification and its benefits will not be\neasily upscaled, as stated so boldly in the abstract.\nThanks for these clarifications. Indeed, these are very valid points and limit the scalability of\nthe use of the organic amendment. We have toned down the phrasing in the abstract and\nincluded additional points in our discussion about the benefits of organic amendments and\nthe potential limitations as follows:\nIn the Abstract:\n“A large-scale implementation of agricultural diversification requires, however, careful\nconsideration of factors that mediate these benefits. Then, agricultural diversification can be\nupscaled for long-term socioeconomic and nature-positive outcomes and, ultimately, for a\nglobal food system transformation.” See L33-37.\nIn the Discussion:\n“Additionally, the effectiveness of the uses of organic amendments depends on factors such\nas the application rate of organic inputs 40,41, as well as the availability of these inputs 42. In\nsome cases, the optimal quantity of organic material required by certain farmlands for\nmaximum benefits 43 might exceed the land's capacity to produce such material. In such\ncases, the use of external organic fertilizers may be necessary and could hinder large-scale\nimplementation of these practices. Therefore, reaping the long-term benefits of these\n9\npractices requires a nuanced consideration of implementation conditions and limitations.” See\nL146-153.\nR2.5 R2-3 The rebuttal on the point about differences in crop yields in studies not normally\ntaking into account the differences in the rotation i.e. single year results from diversified\nsystems. Still needs to be discussed in more detail, in the discussion section ideally. The\nexplanation that these will have been taken into account in the original studies is not credible.\nThanks for this further explanation. We now have included the caveat related to crop yield of\ntemporal diversification along with spatial crop diversification as follows:\n“These benefits may be limited by the non-productive years in case of temporal crop\ndiversification and by land availability for spatial crop diversification 48.” See L162-163.\nR2.6 R2-4 The response somewhat addresses this issue (of land use for more extensive,\ndiversified systems).\nThanks for this comment.\nR2.7 R2-5 The issue of scalability of practices that use organic amendments is still not\nadequately discussed. From the answer I am not convinced that the authors have understood\nmy point, or if they have, they need to be more explicit about the limitations of approaches\nthat require organic amendments to be applied at rates in excess of the production capacity\nof the land. Please try again to explain this more clearly.\nThank you for these additional details. We feel that we have now adequately added them in\nour discussion section about organic amendment practices benefits as follows:\n“Additionally, the effectiveness of the uses of organic amendments depends on factors such\nas the application rate of organic inputs 40,41, as well as the availability of these inputs 42. In\nsome cases, the optimal quantity of organic material required by certain farmlands for\nmaximum benefits 43 might exceed the land's capacity to produce such material. In such\ncases, the use of external organic fertilizers may be necessary and could hinder large-scale\nimplementation of these practices. Therefore, reaping the long-term benefits of these\npractices requires a nuanced consideration of implementation conditions and limitations.” See\nL146-153.\nR2.8 R2-6 The response is not adequate. The statement at line 243-244 is not correct. There\nis an article by Skinner et al. (The impact of long-term organic farming on soil-derived\ngreenhouse gas emissions) about GHG emissions from organic farming systems. Is this\nincluded in the meta-analysis?\nThank you for your attention to detail. Skinner et al.,(2019 ) is indeed a meta-analysis on\nGHG emissions from organic farming and it was included in the paper that we screened.\nHowever, the paper did not use LnRR or RR as effect size measurements and did not share\nits original data. This made it impossible for us to calculate the effect sizes in the same way\n10\nas the other studies. Therefore, we could not include its results in our second-order meta-\nanalysis dataset.\nR2.9 R2-7 I still don’t agree that inoculants are a diversification strategy and would prefer to\nsee this response variable removed from the study.\nWe agree with the reviewer that inoculants differ from other diversification practices\nexamined in our study to a certain extent. However, we prefer to keep inoculants as part of\nthis study for two reasons. First, we follow an established definition of agricultural\ndiversification practices, whereby functional diversity is added to an agricultural system to\nderive benefits. Inoculants are functionally diverse & biological components intentionally\nintegrated into agricultural systems, thus aligning with the applied diversification. Second, our\nstudy aims to build upon existing classifications rather than redefining them and while recent\nstudies somewhat alter established classifications(e.g., Rasmussen et al., 2024), they\nmaintain inoculation as part of diversification, reinforcing our decision to do the same.\nOverall, we feel that ignoring this practice would not only conflict with existing literature but\ncould also limit the impact of our study, because it addresses an emerging area of interest in\nsustainable agriculture. Importantly, omitting inoculation could disadvantage scientists\nworking in this field by weakening the scientific basis for advocating long-term research\nfunding on this practice. By including it, we provide a stronger foundation for further studies\nand discussions on its role within diversification strategies.\nThat said, we remain open to revising our approach should the reviewer provide a robust\nreference that justifies the exclusion of inoculation and guide us to decide on this matter. Our\nconcern is to ensure that our study does not introduce further debate over the definition and\nclassification of diversification practices, but rather contributes meaningfully to the ongoing\ndiscourse in this field.\nR2.10 R2-14 As stated above, I don’t think you can assume that the primary studies have\ntaken into account issues like rotational diversity and the cost across rotational time scales of\ndiversification. I also don’t think the issue of applying organic amendments at rates higher\nthan the production capacity of the land is adequately discussed and I encourage the authors\nto consider both of these issues in more detail in the discussion part of the paper.\nThanks for reiterating the importance of this aspect. We have now included details about this\npoint into our discussion as follows:\n“It is worth noting that, although crop diversification practices do not necessarily increase\ncrop yield compared to conventional practices, they can contribute to food security through\ndiet diversification and might help improve resistance to external shocks and climate change\nadaptation 46–48. These benefits may be limited by the non-productive years in case of\ntemporal crop diversification and by land availability for spatial crop diversification 48.” See\nL158-163.\nR2.11 R2-15 Still issue with not addressing that the area of land needed to produce the\namendments needs to be discussed.\nlimitations as follows:\n“Additionally, the effectiveness of the uses of organic amendments depends on factors such\nas the application rate of organic inputs 40,41, as well as the availability of these inputs 42. In\nsome cases, the optimal quantity of organic material required by certain farmlands for\nmaximum benefits 43 might exceed the land's capacity to produce such material. In such\ncases, the use of external organic fertilizers may be necessary and could hinder large-scale\nimplementation of these practices. Therefore, reaping the long-term benefits of these\npractices requires a nuanced consideration of implementation conditions and limitations.” See\nL146-153.\nR2.12 R2-16 A statistical approach is suggested to account for the problems with studies that\nreport crop yield by species (thus not accounting for non-productive years in their results).\nThis misses my point; I am only asking that this limitation/issue with crop diversification needs\nto be discussed. Systems that are more diversified may have smaller areas of food crops\nwhen scaled up to the landscape. Most of the major studies on crop yields comparing organic\nwith conventional have reported the yields for a single crop, not taking into account the\nproportion of time that it is growing in each system.\nWe have made a caveat in discussion to address this point. See L158-163.\nR2.13 R2-18 My point here relates to the issue raised in several comments above and still\nnot adequately discussed. See response to R2-16 above for my concern; this needs to be\naddressed. Meta-analyses comparing organic with conventional (e.g. Seufert et al 2012, De\nPonti et al ) report yields for a single species in a single year and do not take into account the\nfact that in diversified systems food crops may be grown less frequently in a rotation than for\nmore intensive systems.\nWe have made a caveat in discussion to address this point. See L158-163.\nR2.14 R2-23 As already stated above, I am not suggesting a different analytical approach but\nI would like to see a bit more discussion on some of the fundamental differences in practices\nand how that might impact food production at the landscape scale and also effects on the\nwider food system. Lower yields per unit area in diversified systems and lower areas of food\ncrops being grown at the landscape scale are two likely outcomes of a transition to\nagroecological methods; this needs to be acknowledged and is important to discuss.\nThanks for providing further details on the points we need to consider. We have now included\na sentence about the broader potential caveats of implementation and connected it to other\naspects of the food system in the last paragraph of the long-term implementation section as\nfollows:\n“It also enables broader landscape finance strategies, as our results provide detailed\nevidence of the anticipated trade-offs of agricultural diversification implementation and the\nassociated risks 69. However, implementing diversification also requires careful consideration\nof the factors that mediate individual diversification practices beyond the binary comparison\nbetween monocultures and diversified systems, including geographic variations across\nscales, and climate change, all of which are critical to ensure the benefits of agricultural\ndiversification practices 71–74. Thus, agricultural diversification provides further opportunities\nto shift towards a truly integrated strategy 33, whereby local culture, context and practices are\naccounted for, and ecosystem services are fully utilized for diverse livelihoods beyond crop-\nrelated productions 75. See L320-329.\nR3.1 Thank you for submitting the revised version of your manuscript “Decades matter:\nAgricultural diversification increases financial profitability, biodiversity, and ecosystem services over\ntime”, along with detailed responses to the reviewer comments. I appreciate the effort you have\ninvested in addressing the feedback of my co-reviewers and myself, esp. determining the\nrelationship between effect sizes and duration, as well as going back to the original studies,\nrecalculating effect sizes for those that did not report exact durations, and re-running the\nanalysis.\nWe thank this reviewer for this positive feedback on our extensive efforts based on the\ncomments we received from the previous round of revision.\nR3.2 After going through the responses to comments, I agree with most of them and can\nfollow their argumentation. However, many of your responses are purely argumentative and\ndid not result in changes in the manuscript. I would like to detail this (and additional\ncomments) in the below.\nThanks for these comments. We have now addressed the other points. Please find the\ndetails below each of the comments.\nR3.3 Your response to my comment on geographic and selection bias is understandable, yet\nI would have appreciated that these biases were included and discussed in the manuscript\n(along the lines of several comments made by Reviewer 2), rather than just to me. Moreover,\nI am not fully convinced that extracting geolocations from the original studies would not have\nbeen possible, esp. considering that you extracted effect sizes from those studies. A possible\napproach could be to count the number of studies per country, esp. if exact geolocations are\ndifficult to extract.\nMany thanks for providing this further clarification about the potential effects of geographic\nlocation and the specific suggestions on how we can address this important comment. While\ngoing through the original studies included in each meta-analysis would be possible to get the\ngeographic location of the effect sizes, it is beyond the efforts we can realistically do.\nHowever, we went through all meta-analysis studies included in our dataset again to gather\ninformation on the location of the studies synthesized in each meta-analysis at the country\nlevel. With this approach, we now have identified the location at country level for all 124\nstudies, but we could not derive country-level information for 57 studies. We now mapped the\ndistributions of countries represented in the meta-analysis in our dataset (see below and\nSupplementary Fig. 5 and 6).\nstudies with data from countries worldwide.\nSupplementary Fig. 6. Geographic distribution of meta-analysis studies with long-term (> 20\nyears) effects of agricultural diversification practices and the number of studies with data from\ncountries worldwide.\nvariation of the effects of diversification across geographic locations in the discussion:\n“The geographic distribution of primary studies represented in 123 out 184 meta-analyses\nincluded in our synthesis revealed limited original studies from low and middle income countries\n(Supplementary Fig. 5), highlighting a general geographic bias. For synthesis with long term\neffects of agricultural diversification (>20 years), the original studies from the majority of\ncountries in Africa were underrepresented (Supplementary Fig. 6). This research gap\nhighlights the necessity for more primary studies or at least synthesis of data from these\nregions, and more funding to explore the benefits of diversification practices in these\ncontexts. Addressing this imbalance is critical to enable practitioners to inform decision\nmaking that can then help achieving the Sustainable Development Goals (SDGs) 4,22.\nFurthermore, research has demonstrated that geographic location significantly influences the\noutcomes of diversification practices 47,63, emphasizing the importance of region-specific\nstudies to develop tailored and impactful strategies.” See L270-282.\nR3.4 Along those lines, I would have appreciated a discussion of the effects of landscape\ncontexts as well as changes therein (e.g., climate change) on socio-ecological outcomes of\ndiversification, esp. considering the importance of the former as shown in Rasmussen et al.\n(2024). Even though original studies are lacking that consider both duration and climate\nvariables (or other changing context variables, such as economic conditions), not being able\nto address this is still an important caveat of the analysis and should be discussed.\nThanks for this further specification. We have now included the potential effects of climate\nvariation, landscape context as well as geographic variation into our discussion and study\nlimitation sections, as follows:\n“Furthermore, research has demonstrated that geographic location significantly influences the\noutcomes of agricultural diversification practices 47,63, emphasizing the importance of region-\nspecific studies to develop tailored and impactful strategies.” See L279-282.\n“However, implementing diversification also requires careful consideration of the factors that\nmediate individual diversification practices beyond the binary comparison between\nmonocultures and diversified systems, including geographic variations across scales, and\nclimate change, all of which are critical to ensure the benefits of agricultural diversification\npractices 71–74. Thus, agricultural diversification provides further opportunities to shift towards\na truly integrated strategy 33, whereby local culture, context and practices are accounted for,\nand ecosystem services are fully utilized for diverse livelihoods beyond crop-related\nproductions 75. See L322-329.\nR3.5 I also think that not addressing combined effects of different practices over time on\nsocio-ecological outcomes could and should be discussed.\npractices as follows:\n“Reduced tillage showed slight yield increases over time but significant long-term benefits for\ncarbon sequestration, while organic farming did not provide significant long-term benefits,\nexcept for biodiversity (Fig. 2, Supplementary Table 4). Overall, mainstreaming agricultural\ndiversification and the combination of various practices may be a very effective and long-term\nstrategy for global food system transformation 5,9. For instance, non-crop diversification\nstrategies seem to hold most benefits as an individual practice, while organic farming shows\nan incremental increase in biodiversity benefits over time but has consistently lower yields\n(but see 52). An effective combination of practices may help to transform some of the non-\nsignificant effects into positive long-term benefits 8,9.” See L181-190.\nR3.6In your response R3-27, you argue for “targeted meta-analysis such as the one by\nBeckmann et al. 2019 and local empirical studies for instance on simultaneously testing\nsocial and environmental variable changes”. This does not address my comment, which\ntargeted the statement on the benefits of diversification for simple monocultures. This only\nlooks at one part of the intensity gradient (highly intensified), but neglects areas managed\nwith lower intensity. Such area, however, are crucial for planning, considering their sensitivity\nto intensification regarding yields and biodiversity.\nWe thank this reviewer for this clarification. Although these are very valid points, they cannot\nbe supported by our synthesis due to lack of these details in meta-analysis papers. Now, we\nincluded these aspects in our discussion, speculating that landscape context could influence\nand mediate the outcomes of agricultural diversification as shown by some syntheses:\n“Our work enables farmers to understand the short- and long-term benefits of agricultural\ndiversification compared to conventional agriculture or monocultures. A core argument for its\nimplementation is a sustainable production strategy that can maintain crop yields, increase\nfinancial profitability over time, and benefit the natural environment. It also enables broader\nlandscape finance strategies, as our results provide detailed evidence of the anticipated\ntrade-offs of agricultural diversification implementation and the associated risks 69. However,\nimplementing diversification also requires careful consideration of the factors that mediate\nindividual diversification practices beyond the binary comparison between monocultures and\ndiversified systems, including geographic variations across scales, and climate change, all of\nwhich are critical to ensure the benefits of agricultural diversification practices 71–74. Thus,\nagricultural diversification provides further opportunities to shift towards a truly integrated\nstrategy 33, whereby local culture, context and practices are accounted for, and ecosystem\nservices are fully utilized for diverse livelihoods beyond crop-related productions 75.” See\nL316-329.\nR3.7 Lastly, and importantly, I had difficulties with the way your report your quantitative\nresults on changes in outcome variables. This pertains to the fact that a) your report values\n17\nfor some outcomes (e.g., 189% increase for financial profitability) but none for others (e.g.,\nsoil quality and climate change mitigation), and\nThanks for pointing this out. We have now added percentage values to specify the increase\nin other response variable:\n“The mean effect sizes of soil quality and climate change mitigation, except for climate\nregulation, were significantly positive. Soil fertility, nutrient cycling and carbon sequestration\nhad significant increases over time for (Fig.1), with benefits of 37% to 107% over a 20-year\nperiod (Supplementary Table 2).” See L113-116.\nR3.8 b) the numbers do not fully agree across manuscript sections. For example, you report\nin lines 117-118 “financial profitability, biodiversity, pollination, soil fertility, nutrient cycling,\nand carbon sequestration being 22% to 427% higher after 20 years”. However, in lines 92-93\nyou state “for biological communities, agricultural diversification significantly increased 92\nbiodiversity and pollination benefits by 64% and 629%, respectively”. Moreover, in the\nabstract (lines 25-25) you report “financial profitability, biodiversity, pollination, soil quality,\nand carbon sequestration from 37% to 189% over 20 years”. Why do the value ranges differ?\nWe appreciate your attention to these points. A calculation error was made when determining\nthe percentage of changes from non-temporal to 20-year benefits. The reported increase of\n22% to 427% should have been 22% to 110%. This has now been corrected.\nThese numbers differed from the 64% and 629% increase due to varying levels of\ncomparison. The 22% to 110% range was based on the results of 20-year benefits compared\nto non-temporal effect sizes, whereas the 64% and 629%, as well as 37% to 189%, were\nbased on the comparison of 20-year benefits to short-term (<3 years) effects, as detailed in\nSupplementary Table 2. It should be noted that the 629% resulted from a 10-year benefits\nincrease in pollination compared to short-term (<3 years) effects.\nWe have revised the presentation of these results to clarify the differences in interpretation as\nfollows:\n“For biological communities, agricultural diversification significantly increased biodiversity by\n64% over a 20-year period and pollination benefits by 629% over 10 years (Fig. 1,\nSupplementary Table 2).” See L97-99.\n“The mean effect sizes of soil quality and climate change mitigation, except for climate\nregulation, were significantly positive. Soil fertility, nutrient cycling and carbon sequestration\nhad significant increases over time for (Fig.1), with benefits of 37% to 107% over a 20-year\nperiod (Supplementary Table 2).” See L113-116.\nWe did not include the number 629% in the abstract to tone down the extreme number due to\ndata limitation of pollination services as recommended during the previous round of revision.\n********************************************************\nBeillouin, D., Ben‐ Ari, T., Malézieux, E., Seufert, V., & Makowski, D. (2021). Positive but variable\neffects of crop diversification on biodiversity and ecosystem services. Global Change Biology,\n27(19), 4697–4710. https://doi.org/10.1111/gcb.15747\nJones, S. K., Sánchez, A. C., Beillouin, D., Juventia, S. D., Mosnier, A., Remans, R., & Estrada\nCarmona, N. (2023). Achieving win-win outcomes for biodiversity and yield through diversified\nfarming. Basic and Applied Ecology, 67, 14–31. https://doi.org/10.1016/j.baae.2022.12.005\nRasmussen, L. V., Grass, I., Mehrabi, Z., Smith, O. M., Bezner-Kerr, R., Blesh, J., Garibaldi, L. A.,\nIsaac, M. E., Kennedy, C. M., Wittman, H., Batáry, P., Buchori, D., Cerda, R., Chará, J., Crowder, D. W.,\nDarras, K., DeMaster, K., Garcia, K., Gómez, M., … Kremen, C. (2024). Joint environmental and\nsocial benefits from diversified agriculture. Science, 384(6691), 87–93.\nhttps://doi.org/10.1126/science.adj1914\nSingh, P., Singh, S. K., & Prasad, S. M. (Eds.). (2020). Plant Responses to Soil Pollution. Springer\nSingapore. https://doi.org/10.1007/978-981-15-4964-9\nSkinner, C., Gattinger, A., Krauss, M., Krause, H.-M., Mayer, J., Van Der Heijden, M. G. A., & Mäder,\nP. (2019). The impact of long-term organic farming on soil-derived greenhouse gas emissions.\nScientific Reports, 9(1), 1702. https://doi.org/10.1038/s41598-018-38207-w\nTamburini, G., Bommarco, R., Wanger, T. C., Kremen, C., Van Der Heijden, M. G. A., Liebman, M., &\nHallin, S. (2020). Agricultural diversification promotes multiple ecosystem services without\ncompromising yield. Science Advances, 6(45), eaba1715.\nhttps://doi.org/10.1126/sciadv.aba1715\nReviewer #2 (Remarks to the Author):\nThanks to the authors for their hard work in addressing my comments. I am now satisfied that my\nmajor concerns re: 1. impacts of crop diversification strategies on yields over time and space, and 2.\nissue of scaling up high application rates of organic amendments, have been adequately discussed.\nThe final statement in the abstract has also been moderated. I expect this paper to generate a lot of\ninterest and discussion within the agricultural and ecological research communities.\nResponse:\nThank you very much for your thoughtful feedback and positive comments. We are glad that our\nrevisions addressed your main concerns. We are also grateful for your valuable comments and\nsuggestions in the previous rounds, which have substantially improved the quality and clarity of our\nmanuscript.\nReviewer #3 (Remarks to the Author):\nDear authors,\nI would like to thank you for another very thorough revision of your manuscript “Decades matter:\nAgricultural diversification increases financial profitability, biodiversity, and ecosystem services over\ntime”. I am satisfied with your responses and recommend the manuscript for publication.\nResponse:\nThank you very much for your kind and encouraging feedback. We greatly appreciate your thorough\nreview and thoughtful recommendations throughout the process. Your insights have been invaluable\nin improving the manuscript, and we are delighted that you now recommend it for publication.\nTwo small comments though:\n1) After discovering a calculation error quite late in the submission process, I recommend double-\nchecking all reported values (if not done already, of course).\nResponse:\nThank you very much for this helpful reminder. We have carefully rechecked all calculations and\nreported values to ensure their accuracy and consistency throughout the manuscript and\nsupplementary materials.\n2) I suggest to avoid using the term \"developing countries\" in the manuscript, as it can be seen as\ncondescending towards the people and economies of those countries. Instead, although not perfect\nas well, you could use \"low income countries\" / \"low and middle income countries\".\nResponse:\nThank you for this thoughtful suggestion. We fully agree and have replaced the term “developing\ncountries” with “low and middle income countries” throughout the manuscript to ensure more accurate\nand respectful language.\n1"}, {"round": 3, "reviewer_comments": {"reviewer_2": "Thanks to the authors for their hard work in addressing my comments. I am now satisfied that my major concerns re: 1. impacts of crop diversification strategies on yields over time and space, and 2. issue of scaling up high application rates of organic amendments, have been adequately discussed. The final statement in the abstract has also been moderated. I expect this paper to generate a lot of interest and discussion within the agricultural and ecological research communities.", "reviewer_3": "Dear authors,\nI would like to thank you for another very thorough revision of your manuscript “Decades matter: Agricultural diversification increases financial profitability, biodiversity, and ecosystem services over time”. I am satisfied with your responses and recommend the manuscript for publication.\nTwo small comments though:\n1) After discovering a calculation error quite late in the submission process, I recommend double-checking all reported values (if not done already, of course).\n2) I suggest to avoid using the term \"developing countries\" in the manuscript, as it can be seen as condescending towards the people and economies of those countries. Instead, although not perfect as well, you could use \"low income countries\" / \"low and middle income countries\"."}, "author_response": "Response:\nThank you very much for your thoughtful feedback and positive comments. We are glad that our revisions addressed your main concerns. We are also grateful for your valuable comments and suggestions in the previous rounds, which have substantially improved the quality and clarity of our manuscript.\n\nResponse:\nThank you very much for your kind and encouraging feedback. We greatly appreciate your thorough review and thoughtful recommendations throughout the process. Your insights have been invaluable in improving the manuscript, and we are delighted that you now recommend it for publication.\n\nResponse:\nThank you very much for this helpful reminder. We have carefully rechecked all calculations and reported values to ensure their accuracy and consistency throughout the manuscript and supplementary materials.\n\nResponse:\nThank you for this thoughtful suggestion. We fully agree and have replaced the term “developing countries” with “low and middle income countries” throughout the manuscript to ensure more accurate and respectful language."}]

Decision letter