Landscape and crop management strategies to conserve pollination services and increase yields in tropical coffee farms

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Abstract

Agricultural intensification has reduced biodiversity and leads to fundamental trade-offs between food production and conservation. Conventional approaches to food production are thus no longer suitable. In the present work, we discuss the influence of local management and landscape context variables on coffee yield and crop pollination services. We used 34 coffee farms (15 with low impact and 19 with high impact management) located in Chapada Diamantina, Bahia, Brazil. We analysed the floral visitor patterns and yield and their relationships with landscape and management context over two years. Using a GLM analysis, we found that farms close to natural areas and with low management intensity have higher potential to reduce yield gaps and maintain biodiversity. Biodiversity in turn (represented here by pollinators) improved yields by 30%, and yields were lower on larger, intensively managed farms. Low impact farms, on the other hand, may depend not only on diversified landscapes but also on proper investment in sustainable production practices. Combining landscape and management strategies should thus generate synergies between multiple ecosystem services, such as pollination, yield, farm profitability, and others not analysed here, such as natural enemies and nutrient cycling, among others.

Introduction

Global agricultural production was increased substantially by the introduction of new lands into continuous farming, the intensive use of off-farm inputs (fertilizers, pesticides, machinery), and the use of genetically modified crops, mostly after the “Green Revolution”. However, new strategies to increase crop yields are needed to meet the current projections of global population growth. Moreover, the techniques utilized previously, such as intensive use of pesticides, have led to major losses in global biodiversity, leading to fundamental trade-offs between food production and conservation. Recent research demonstrates that conventional high input strategies are no longer suitable because the differences in crop yields between high and low-yielding farms in a given region (i.e., yield gaps) are increasing (Aizen et al., 2009). Yield gaps arise from multiple causes, including deficiencies in the supply of nutrients or pollination. Yet the ever-increasing input of nutrients and organic matter, or increases in cropping intensity and the expansion of irrigated area, are costly and may only bring about ever diminishing returns. Thus, researchers have been advised to focus on identifying the specific causes of yield gaps in order to develop sustainable and profitable alternatives to existing measures.

A new strategy to address the biodiversity-production trade-off is to optimize or improve crop yields at the same time as enhancing biodiversity, or at least minimize negative impacts, a paradigm also known as “ecological intensification”. These strategies, however, are not so simple, because they require an understanding of complex relationships between the biological community composition and ecosystem function in contrasting management and landscape-level scenarios.

It has been suggested that trade-offs between food production and conservation areas are more likely to be alleviated through an optimal spatial arrangement (Fischer et al., 2008, Phalan et al., 2011, Gabriel et al., 2013, Hulme et al., 2013, Tuck et al., 2014, Ekroos et al., 2016). This could potentially include the combination of high-yield agriculture with areas of protected natural habitat (Ramankutty and Rhemtulla, 2012, Ekroos et al., 2016) or the integration of biodiversity conservation and crop production in the same area, such as in agroecosystems. There is no consensus yet for the best strategy. The best type of farming for biodiversity conservation seems to be dependent on the demand for agricultural products and how pollinator communities change with agricultural yield. The high chemical inputs of pesticides and nitrogen used to assure high yield on conventional farms leads to side effects, such as soil and water pollution (Potts et al., 2010, Foley et al., 2011). Agroecosystems, on the other hand, tend to present lower yields, requiring a larger land area for production.

Biodiversity and yield patterns are influenced not only by management and landscape context, including different spatial scales but also by the type of crop being grown and geographic region, further increasing the complexity of the relationship between crop production and conservation. Empirical studies linking landscape aspects, local management and ecosystem services are still scarce (Kremen, 2015), especially for some groups of species, such as pollinators.

Pollination is an example of an ecosystem service on which agricultural production is highly dependent, determining the yield in 75% of important global crop species. In coffee (arabica variety), although not considered a dependent crop since the plants are autogamous, pollinators can increase productivity (31% on average). Even so, despite its importance, pollination has been largely neglected in studies analysing yield gaps. Crops located far from natural areas, for example, may suffer losses in pollinators, stability, and production (Garibaldi et al., 2011b). However, to what extent this can be influenced by other landscape aspects such as patch diversity and crop management still requires further investigation.

In this study, we compared the influence of local management and landscape context variables on coffee yield and crop pollination services. We tested the following hypotheses using the approach described above: (i) floral visitor patterns and yields can be explained and influenced by differences in landscape and management context; and (ii) floral visitor composition also influenced coffee yields. We then examined what type of landscape-level scenario and management is the most suitable for biodiversity conservation and production purposes using coffee farms in Chapada Diamantina, Bahia, Brazil as a practical model.

Section snippets

Study area and selection of sampling units

The present study was conducted on coffee farms located in the cities of Mucugê and Ibicoara in the Chapada Diamantina region, Bahia, Brazil (limits: 41°42′11” W, 12°43′36” S; 41°15′5” W, 12°43′52” S; 41°42′51” W, 13°44′8” S; 41°15′40” W, 13°44′ 23” S, altitude between 900 and 1400 m; Fig. 1). This region has an average annual precipitation of 1379 mm, an annual average maximum temperature of 25.7 °C, with a minimum temperature of 16 °C (2013 to 2014 local weather station data from the

General findings

A total of 530 individuals from 84 insect species (bees and non-bees) were sampled within coffee farms distributed in eight different orders (Hymenoptera, Coleoptera, Diptera, Hemiptera, Lepidoptera, Neuroptera, Odonata, and Orthoptera) (Table 1). Bees (Hymenoptera: Apoidea, 19 species), were the most abundant group (72.6% of 530). Per farm site, the average total visitor richness was 4 ± 2.08 (species) and average total abundance was 70 ± 139.8 (floral visitors) (Appendix D).

Floral visitor richness

Across all fields,

Discussion

As expected, farms located far from natural or semi-natural areas had lower visitor richness and abundance. Similar results were found in previous studies in which crop isolation was an important cause of crop stability losses (Ricketts et al., 2004, Carvalheiro et al., 2010, Garibaldi et al., 2011b). In addition, low impact management, with no or low use of pesticides, was also related to a higher level of biodiversity. Landscapes composed of low impact coffee fields close to natural areas

Acknowledgements

We thank CNPq – INCT-IN-TREE (465767/2014-1) and PVE – Ciência sem fronteiras (407152/2013-0) for financial support. We would also like to express our thanks to Capes for the Ph.D. scholarship granted to J. Hipólito and to CNPq for the research fellowship granted to B.F. Viana. We are thankful to all the farmers for letting us work in their fields and to the field assistants for their invaluable support. We thank all the researchers for insect identification: T. Mahlmann (INPA), M. Barbosa

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