Effects of Organic and Conventional Agriculture on Sustainability Metrics.

Overall, organic systems had greater biotic abundance (mean effect size = 0.32, 90% CI: 0.19 to 0.45) (t₁₀₁ = 4.01, P = 0.0001) and biotic richness (mean effect size = 0.32, 90% CI: 0.22 to 0.43) (t₉₃ = 5.88, P < 0.0001) than conventional systems (Fig. 2). These results are consistent with previous meta-analyses on the effects of organic compared to conventional agriculture on abundance and diversity of many organismal groups (5⇓–7). Compared to conventional systems, organic agriculture had lower overall yields (mean effect size = −0.27, 90% CI: −0.32 to −0.22) (t₁₅₈ = −9.03, P < 0.0001) but greater profitability (mean effect size = 0.59, 90% CI = 0.45 to 0.73) (t₃₆ = 7.15, P < 0.0001) (Fig. 2), as also shown by other meta-analyses (2⇓–4).

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Fig. 2.

Effect size (mean ± SE) in organic vs. conventional systems. Shown are results for biotic abundance (n = 102), biotic richness (n = 94), yield (n = 159), and profit (n = 37). An effect size of 0 indicates no difference between organic and conventional systems. Values above 0 indicate a higher value for the sustainability metric in organic systems; values below 0 indicate a higher value for the sustainability metric in conventional systems.

Effects of Landscape Context on Biotic Communities.

The benefits of organic farming for biotic abundance and richness were best predicted by average crop field size in the landscape (abundance: SI Appendix, Tables S16–S21; richness: SI Appendix, Tables S22–S27; Fig. 3 A–D). The Akaike weight (ω) for field size was >0.67 for all abundance models and >0.85 for all richness models, and field size had regression coefficients that did not overlap 0 in all models (SI Appendix, Tables S16, S18, S20, S22, S24, and S26); field size was also included in the most well-supported models (∆AICc < 2.0) for abundance and richness (SI Appendix, Tables S17, S19, S21, S23, S25, and S27). Other landscape metrics, including percent cropland (including the quadratic term), Shannon’s diversity index, and patch richness had ω values <0.39 in all cases; these variables were also not included in the well-supported models except for percent cropland in abundance models (SI Appendix, Tables S16–S27). Overall, a one-unit increase in average crop field size resulted in a 3.1% and 2.3% increase in the biotic abundance or richness, respectively, in organic relative to conventional systems (Fig. 3 A and B). Effects of field size on abundance in organic compared to conventional systems were consistent across organism groups (χ² = 9.5, degrees of freedom [df] = 7, P = 0.22), functional groups (χ² = 8.1, df = 7, P = 0.32), continents (χ² = 0.61, df = 4, P = 0.96), biomes (χ² = 3.5, df = 4, P = 0.47), crop types (χ² = 4.2, df = 7, P = 0.76), and level of development (χ² = 0.0060, df = 1, P = 0.93). Similarly, effects of field size on richness differences between organic and conventional systems were consistent across organism groups (χ² = 5.2, df = 8, P = 0.73), functional groups (χ² = 7.7, df = 10, P = 0.66), continents (χ² = 4.5, df = 3, P = 0.22), biomes (χ² = 0.69, df = 3, P = 0.87), crop types (χ² = 0.92, df = 6, P = 0.99), and level of development (χ² = 0.66, df = 1, P = 0.42).

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Fig. 3.

Relationship between crop field size, biotic abundance, and biotic richness. (A and B) Best-fit regression (and 90% CIs) showing relationship between average crop field size based on Fritz et al. (49) and the log response-ratio effect sizes for (A) biotic abundance and (B) biotic richness. (C) Map showing predicted effect size of biotic abundance in organic vs. conventional systems with varying field sizes (SI Appendix, Fig. S14A) and (D) map showing predicted effect size for biotic richness in organic vs. conventional systems with varying field sizes (SI Appendix, Fig. S14A). Coefficients used to generate regressions and maps were based on the most-well-supported simple statistical models (biotic abundance: SI Appendix, Table S16; biotic richness: SI Appendix, Table S22).

Studies conducted in conventional and organic farming systems show that crop field size often has a negative correlation with the abundance and diversity of taxa such as plants, birds, and arthropods (17, 18, 25, 26). Our results show a similar effect of crop field size in boosting the biodiversity benefits of organic farms, but we found that percent cropland, the most widely used landscape context variable in the agroecological literature (5⇓–7, 12, 13), was not significant. These results occurred despite the low resolution of the global field size layer (1 km) compared to the percent cropland layers for the United States and Europe (∼30 m). Moreover, our data did not strongly support the intermediate landscape complexity hypothesis, as quadratic effects of percent cropland or crop field size were not significant or of high weight in any biotic models (SI Appendix, Tables S16–S27). While the intermediate landscape complexity hypothesis has typically been evaluated only in the context of percent cropland, crop field size can also reflect intensification, but the lack of quadratic effects of both of these variables suggest trends were largely linear. Nonetheless, our results provide further evidence supporting recent studies that show that aspects of landscapes associated with configurational heterogeneity, such as crop field size, can affect communities as much or more than compositional heterogeneity variables such as percent cropland (17, 18, 25, 26).

Crop field size may reflect agricultural intensification, as areas with larger fields often have greater agricultural investment, mechanization, and labor intensity (27). We show that crop field size was also positively correlated with the percent cropland (Fig. 4A) but negatively correlated with crop patch richness (Fig. 4B), crop diversity (Fig. 4C), and natural habitat diversity (Fig. 4D and SI Appendix, Table S28). This suggests regions with larger crop fields were more intensified. Crop fields were largest in North America and smallest in Asia, with South America and Europe having intermediate-sized fields (SI Appendix, Fig. S10A). Crop fields were also larger in developed relative to less-developed countries (SI Appendix, Fig. S10B) and in temperate compared to tropic or desert biomes (SI Appendix, Fig. S10C). These trends match observations that agricultural intensification has occurred most extensively in temperate regions such as North America and in more developed countries (28). This may be important because larger conventional crop fields are often associated with a greater use of synthetic inputs (fertilizers and pesticides) (29) that can harm biological communities (30, 31). As organic farms typically use fewer pesticides than conventional farms (1), our results may reflect that organic farming is most beneficial in boosting biodiversity in landscapes where biological communities have been harmed by agricultural intensification (i.e., landscapes with large crop fields). Put another way, our results suggest that organic agriculture may provide a refuge for organisms in intensified landscapes. Our maps suggest that organic agriculture in the upper midwestern United States, eastern Europe, and western Asia may promote biodiversity to the largest degree compared to conventional systems (Fig. 3 C and D), although our data extent makes it difficult to extrapolate these results to tropical or less-developed regions (SI Appendix, Tables S11 and S12).

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Fig. 4.

Relationship between crop field size and various metrics of landscape context. Correlations (and 90% CIs) showing the relationship between average crop field size and (A) percent cropland, (B) crop patch richness, (C) crop diversity (Shannon’s diversity index, SHDI), and (D) natural habitat diversity (SHDI) in the broader landscape. See SI Appendix, Table S28 for statistical output of models.

Small fields also have greater edge-to-area ratios than large fields, and a greater proportion of the area of small fields resides in close proximity to a field border. This can benefit organisms that require multiple cover types, as they can move more easily between small compared to large fields (landscape complementation) (17, 18). One study showed the majority of organisms in agroecosystems accumulate near field edges, and regions with small fields received the greatest benefits from organic practices because organisms had access to the majority of field borders (18). Our results instead show the opposite trend: large fields promoted the greatest biodiversity benefits of organic sites. This may occur if organisms can more easily move from conventional fields into natural habitat patches following disturbances, such as pesticide sprays, if fields are small compared to large. If organisms are less capable of escaping conventional farms when they are large they could incur greater harm from disturbances, which could elevate the biodiversity benefits of organic systems. This would support the hypothesis that boundary zones connecting habitats are key for supporting biodiversity in agroecosystems (32, 33).

Effects of Landscape Context on Yield.

Empirical studies show that greater biodiversity on farms can promote higher crop yield (34⇓⇓–37). In turn, positive effects of organic farming on biodiversity might translate into greater yields on organic compared to conventional farms. This could occur due to more effective pest control if organic farms contain more natural enemy species (ref. 34, but see ref. 20), or if organic farms with greater pollinator diversity have increased fruit set (36). Moreover, greater plant diversity in agroecosystems is often associated with decreased pest densities (35). However, while organic systems boosted biotic abundance and richness particularly in landscapes with large crop fields, the yield gap between organic and conventional systems was not strongly affected by landscape metrics in the simple models (SI Appendix, Tables S29 and S30). However, in more complex models that included compositional heterogeneity metrics (either Shannon’s diversity index or patch richness), the quadratic term for field size had a significant negative coefficient, indicating a downward-facing curve (SI Appendix, Tables S31–S34). In simple and complex model sets, the field size metric had the highest ω and was included in most well-supported models (SI Appendix, Tables S29–S34 and Fig. S11). Effects of field size on yield were robust across continents (χ² = 4.7, df = 3, P = 0.20), biomes (χ² = 5.0, df = 3, P = 0.17), crop types (χ² = 12.5 df = 7, P = 0.084), and development level (χ² = 1.1, df = 1, P = 0.29).

We could not explore direct relationships between effects of organic systems on yield and biotic abundance or biotic richness compared to conventional agriculture, given that too few studies examined all of these metrics. However, the differential responses of these sustainability metrics to landscape context suggests that they may not be strongly linked in the field. One possible explanation is that the benefits of organic farming are typically greatest for rare species (7), and these rare species may not play key roles in providing ecosystem services (7, 38). If the benefits of organic compared to conventional farming primarily accrue for rare species that contribute little to ecosystem services such as yield, this may explain why benefits of organic farming for biodiversity did not translate into greater yields on organic farms. Moreover, it remains unclear whether biotic abundance and biotic richness gains in organic compared to conventional farms were primarily due to beneficial taxa, such as pollinators and predators, or harmful pests, or a combination of both. If gains to abundance and richness of ecosystem service providers are offset by similar gains in pest densities at the same sites, benefits to yield may be minimal (8, 20, 22). Yet, if gains to abundance and richness on organic farms are greatest for pests, it may partially explain why biodiversity metrics and yield responded in the opposite direction to crop field size. Prior meta-analyses show that weeds are more abundant in organic systems (6), but pollinators and natural enemies are boosted (5, 7), suggesting simultaneous gains in pest and beneficial species likely occur on organic farms.

We only observed strong effects of landscape context on yield in complex models, such that our results for effects of crop field size were not as robust as with the biodiversity metrics; the magnitude of the field size effect was also smaller for yield (SI Appendix, Fig. S11) compared to biotic abundance and richness (Fig. 3 A and B). This suggests that the productivity of organic compared to conventional farms is mediated primarily by on-farm management. Organic farmers may be able to reduce yield gaps with comparable conventional systems by using multicropping, longer crop rotations, and more nitrogen fertilizers (2, 3). Yield gaps can also be reduced when organic farms incorporate perennial crops or legumes, when soils have a neutral pH, and in rain-fed systems (2, 3). Farms with high water-use efficiency and effective weed control also typically have higher yields (22). Yield gaps between conventional and organic farms may also be dependent on crop type; fruit crops tend to have little to no yield gap, while vegetable and cereal crops tend to have the largest gap (2, 3). Overall, our results suggest that yield gaps may be more influenced by on-farm practices than by landscape context, the former of which can be controlled by growers.

Perhaps the biggest criticism of organic farming is its lower yields relative to conventional agriculture (39, 40). However, some contend that environmental advantages of organic systems far outweigh lower yields and that increasing research and breeding resources for organic systems would reduce yield gaps (41, 42). Globally, enough food is produced to more than feed the world’s population, but inequities in food distribution do not allow for adequate access to all individuals (43). More and more, scientists are arguing that we need to consider multiple sustainability indicators other than crop yield to better promote global food security (44, 45). In that light, although organic systems produce lower yields than conventional agriculture, they are more profitable and environmentally friendly and deliver equal or more nutritious foods with fewer pesticide residues (1⇓⇓–4). Our study suggests that a singular focus on yield may cause other factors that differ between organic and conventional farms to be overlooked.

Effects of Landscape Context on Financial Performance.

While yield gaps were not strongly affected by landscape context, the relative profitability of organic systems had a significant negative relationship with crop field size (Fig. 5 and SI Appendix, Tables S35, S37, and S39 and Fig. S12). Average crop field size had a ω >0.79 in both simple and complex models (SI Appendix, Tables S35, S37, and S39). Crop field size was also the only factor retained in the most-well-supported models (SI Appendix, Tables S36, S38, and S40). The percent cropland, Shannon’s diversity index, patch richness, and interactions between these variables and field size always had a ω <0.27 (SI Appendix, Tables S35–S40). Effects of field size on profitability were stronger in cereals and oil crops than in vegetables, with intermediate effects for fruits and legumes (χ² = 16.5 df = 4, P = 0.0024).

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Fig. 5.

Relationship between landscape context, profitability, costs, and price premiums. Maps showing predicted effect sizes comparing organic vs. conventional farming for (A) profits, (B) production costs, and (C) organic price premiums (based on crop field size and/or percent cropland; see SI Appendix, Fig. S14; regression coefficients for these landscape factors were taken from most-well-supported simple statistical models; see SI Appendix, Tables S35, S41, and S47). All of these variables were highly correlated. Only the predictions for the United States are shown given that this was the geographic scope of our input studies; interpolations beyond this region would not be supported by our dataset.

However, our metadataset for profitability only contained studies conducted in the United States. Few studies on profitability were conducted outside this country, and most of those that existed did not contain landscape coordinates. Thus, while our biotic abundance, biotic richness, and yield analyses reflected global trends (although data were primarily from the United States, Canada, and Europe, hindering inference about tropical systems), our results for profitability were limited to the United States. To our knowledge, the only other study that has examined the influence of landscape context on agricultural system profitability was conducted on wheat production in landscapes of West and East Germany following World War II (18). This study found that organic farming experienced the greatest profit gain in less-intensified landscapes of West Germany that had smaller field sizes compared to East Germany, in line with our findings.

The main factors that determine the profitability of organic agriculture are price premiums, crop yields, labor costs, and potential cost savings from reduced inputs (4). Our results reinforce this finding, given that increased profitability of organic compared to conventional systems was positively correlated with reduced costs (SI Appendix, Fig. S13A), lower yield gaps (SI Appendix, Fig. S13B), and greater price premiums (SI Appendix, Fig. S13C); the negative effect of crop field size on profitability also mirrors the effect on yields (SI Appendix, Fig. S11). Thus, we assessed how each of these metrics responded to landscape context to assess mechanisms that might explain the complex relationship between landscape context (crop field size, percent cropland, and landscape heterogeneity) and profitability. Production costs, price premiums, and benefit/cost ratios responded in qualitatively similar ways to average crop field size, although percent cropland and landscape diversity (Shannon’s diversity index and patch richness) were also included in some of the most-well-supported models for costs and price premiums unlike benefit/cost ratios (Fig. 5 and SI Appendix, Tables S35–S52). Thus, variation in crop yields, farm production costs, and organic price premiums across landscapes appears to be the primary driver of landscape-level effects on profitability of organic compared to conventional systems.

Given that profitability varies across landscapes in response to production costs and price premiums received in different landscapes, understanding factors that shape organic markets for producers (which differ from consumer markets) and consumer willingness to pay a premium for organic foods is critical. Profitability will be highest in areas with large numbers of consumers of organic foods, or greater supplies of organic inputs, which may often occur in areas with greater population density and median income (46). Moreover, areas with lower property taxes and those closer to interstates tend to have larger organic markets for producers (46). In more rural areas with less infrastructure, consumer density may be too low to support markets for organic goods with premiums, and farmers who sell their crops in these regions may receive lower premiums.

Win–Win Scenarios and Knowledge Gaps.

We found that large crop field sizes were associated with greater biotic abundance and biotic richness benefits in organic compared to paired conventional systems (SI Appendix, Tables S16–S27 and Fig. 3) but lower yield and profitability benefits (Fig. 5 and SI Appendix, Tables S29–S40 and Figs. S11 and S12). However, while benefits of organic farming for biodiversity were greatest in landscapes with large crop fields, organic farming promoted biodiversity across all landscape contexts analyzed (effect sizes >0 for any crop field size; Fig. 3 A and B). Areas with small crop fields also had the lowest yield gap between organic and conventional farms and greatest profitability benefits. Regions with small crop fields may thus promote “win–win” scenarios where organic farming boosts biodiversity and profitability with minimal yield gaps compared to conventional farming. Yet, our dataset only allowed us to assess such “win–win” scenarios in the United States and are limited in predicting such scenarios for other regions, particularly the tropics, from which we have few data and in which systems may be quite different (Fig. 1). More specifically, our datasets for biotic communities (81 total studies) and yield (78 total studies) were considerably larger than our profitability dataset (9 studies), with more data being needed for profitability from systems outside the United States to more effectively explore potential “win–win” scenarios.

We note that we compared the magnitude of differences in biodiversity and profitability in organic vs. conventional systems rather than absolute values. Thus, biodiversity may be relatively impoverished in organic systems in more-intensified landscapes compared to organic systems in less-intensified regions, such as shade-grown organic coffee, while profits in the United States might be higher than in other regions. Moreover, although most of our studies were conducted in homogeneous landscapes with little variability across sites (SI Appendix, Table S15), some studies were conducted along landscape gradients such that we calculated average landscapes per study. This may confound results if average landscape metrics poorly characterized the overall landscape, which might occur if landscape variables such as crop field size were nonnormally distributed. Finally, we note that our results may have been biased by different resolutions of land cover data used in the United States, where all of our profitability studies were conducted, compared to the more global analysis of biotic communities; however, we used the best available data by region. More studies that assess the benefits of organic farming on biotic communities, yield, and profitability in the same ecological context, but spanning landscape gradients, would aid in identifying “win–win” scenarios for farmers in areas with growing organic markets.