Deciphering the Biodiversity–Production Mutualism in the Global Food Security Debate
Authors: Ralf Seppelt, Channing Arndt, Michael Beckmann, Emily A. Martin, Thomas W. Hertel
Journal: Trends in Ecology & Evolution (2020)

Increasing demands for agricultural commodities are resulting in more intensely managed landscapes. This is at odds with biodiversity conservation and largely ignores farmland biodiversity's supporting function for high and stable yields.An overhaul of agroeconomic models to account for the biodiversity-production mutualism is urgently needed to answer a question of utmost importance: How do we manage the resources of our planet in such a way that we produce enough healthy food without destroying our life-support system?A comprehensive analytical framework is provided that accounts for multitrophic biodiversity-production processes; bridges disciplinary boundaries between agronomy, agroecology, economics, and conservation science; and elucidates the strong interactions of ecosystem functioning with food security and malnutrition. Without changes in consumption, along with sharp reductions in food waste and postharvest losses, agricultural production must grow to meet future food demands. The variety of concepts and policies relating to yield increases fail to integrate an important constituent of production and human nutrition – biodiversity. We develop an analytical framework to unpack this biodiversity-production mutualism (BPM), which bridges the research fields of ecology and agroeconomics and makes the trade-off between food security and protection of biodiversity explicit. By applying the framework, the incorporation of agroecological principles in global food systems are quantifiable, informed assessments of green total factor productivity (TFP) are supported, and possible lock-ins of the global food system through overintensification and associated biodiversity loss can be avoided. Without changes in consumption, along with sharp reductions in food waste and postharvest losses, agricultural production must grow to meet future food demands. The variety of concepts and policies relating to yield increases fail to integrate an important constituent of production and human nutrition – biodiversity. We develop an analytical framework to unpack this biodiversity-production mutualism (BPM), which bridges the research fields of ecology and agroeconomics and makes the trade-off between food security and protection of biodiversity explicit. By applying the framework, the incorporation of agroecological principles in global food systems are quantifiable, informed assessments of green total factor productivity (TFP) are supported, and possible lock-ins of the global food system through overintensification and associated biodiversity loss can be avoided. 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Management of agricultural landscapes serves the provisioning of agricultural goods and has had mostly negative impacts on biodiversity [7.Beckmann M. et al.Conventional land-use intensification reduces species richness and increases production: a global meta-analysis.Glob. Chang. Biol. 2019; 25: 1941-1956Crossref PubMed Scopus (55) Google Scholar,8.Newbold T. et al.Global effects of land use on local terrestrial biodiversity.Nature. 2015; 520: 45-50Crossref PubMed Scopus (1445) Google Scholar]. This occurs through both the expansion and intensification of cultivated areas, which in turn warps the composition of landscapes and their structure. Conventional intensification to increase yields is typically done by homogenizing the landscape (fewer, larger fields), increasing inputs (labor, fertilizer, irrigation, chemicals), and/or augmenting harvest intensities [26.Levers C. et al.Archetypical patterns and trajectories of land systems in Europe.Reg. Environ. 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This requires a broader perspective that includes management of landscapes. The positive effect of intensifying land management on yields is well studied. The relevant range of classical reductionist production functions is regarded as positively sloped in input intensification, as no rational agent would purchase inputs to reduce production (Figure 1A ). However, groups of farmers operating in interlinked agricultural landscapes may find that their individual choices collectively reduce productivity because each operator ignores the mutualism of biodiversity and production (Figure 4.1 in [39.Fuglie K. et al.G20 MACS White Paper: Metrics of Sustainable Agricultural Productivity. Organisation for Economic Cooperation and Development, 2016Google Scholar]). Hence, the yield function in intensification may become flat or even turn negatively sloped when BPM is considered (Figure 1B). This suggests a revision to agroeconomic models. In standard models, each input is usually weighted according to its economic contribution, and an index of all outputs can be obtained by weighting each crop according to its share in the total economic value. If the output index increases faster than the input index, total factor productivity (TFP) increases [40.Fuglie K. R&D capital, R&D spillovers, and productivity growth in world agriculture.Appl. Econ. Perspect. Policy. 2018; 40: 421-444Crossref Google Scholar]. TFP was proposed to provide a metric for agricultural sustainability [41.Lynam J.K. Herdt R.W. Sense and sustainability: sustainability as an objective in international agricultural research.Agric. Econ. 1989; 3: 381-398Crossref Google Scholar,42.Coomes O.T. et al.Leveraging total factor productivity growth for sustainable and resilient farming.Nat. Sustain. 2019; 2: 22-28Crossref Scopus (27) Google Scholar], but since the output and input measures typically cover only those aspects for which markets exist, nonmarket implications are ignored [43.Gollop F. Swinand G.P. Total resource productivity. Accounting for changing environmental quality.in: Hulten C.R. New Developments in Productivity Analysis. University of Chicago Press, 2001: 587-608Crossref Google Scholar]. TFP growth can be neutral or beneficial to biodiversity. For example, pest- or disease-resistant varieties can achieve the same yields with reduced use of potentially harmful chemicals. Here, the technology can have positive external effects (e.g., improved health or reduced chemical runoff) [44.Ahmed A.U. et al.Impacts of Bt Brinjal (Eggplant) Technology in Bangladesh. International Food Policy Research Institute, 2019Google Scholar]. However, new technologies are not always environmentally friendly. For example, the use of the dicamba weed killer in conjunction with new soybean varieties has led to numerous lawsuits from people who suffered collateral damage from drifting dicamba [45.Nickel R. Polansek T. Battle of the beans: Monsanto faces a fight for soy market. Reuters Business News, 2018Google Scholar]. These negative results due to the new technology would be ignored in traditional TFP approaches but would be captured by a green TFP approach – also termed total resource productivity (TRP) [43.Gollop F. Swinand G.P. Total resource productivity. Accounting for changing environmental quality.in: Hulten C.R. New Developments in Productivity Analysis. University of Chicago Press, 2001: 587-608Crossref Google Scholar]. Green TFP or TRP include negative outputs (such as pollution or biodiversity loss) and inputs based on natural resources (such as groundwater or biodiversity) valued for their societal contribution rather than at their (often lower or zero) market value. Green TFP has been suggested as a more appropriate performance measure. However, attempts at operationalization have frequently failed [39.Fuglie K. et al.G20 MACS White Paper: Metrics of Sustainable Agricultural Productivity. Organisation for Economic Cooperation and Development, 2016Google Scholar], partly due to missing indicators but also due to a lack of available modeling concepts. Applying the BPM concept can inform estimates of green TFP [46.Ehui S.K. Spencer D.S.C. Measuring the sustainability and economic viability of tropical farming systems: a model from sub-Saharan Africa.Agric. Econ. 1993; 9: 279-296Google Scholar]. A landscape-related perspective that incorporates BPM has rarely been brought to bear in production agriculture, for at least two reasons. First, the missing indicators problem – valuation of nonmarket inputs and outputs – is challenging because the details of the BPM relationship are complex and location specific. Second, relatively few individual farms operate at the scale where taking a landscape-related perspective results in private gains to the owner/operator of the farm. As shown in Figure 1, recognizing the BPM concept opens the possibility that, for intensively operated land, biodiversity gain may coincide with minor yield losses; or, for extensively operated land, substantial yield gain can occur with limited intensification if biodiversity, and hence the BPM relationship, is maintained. Indeed, gains along both dimensions may be possible [47.Kremen C. Merenlender A.M. Landscapes that work for biodiversity and people.Science. 2018; 362eaau6020Crossref PubMed Scopus (229) Google Scholar], for instance, with positive effects of intercropping, as shown in a recent global-scale meta-analysis [48.Li C. et al.Syndromes of production in intercropping impact yield gains.Nat. Plants. 2020; 6: 653-660Crossref PubMed Scopus (46) Google Scholar]. To implement and test the BPM concept, substantially revised agroecological models and their implementation in an analytical framework are required. Box 1 illustrates how the mutual feedbacks between biodiversity and production can be integrated into regional or global agroeconomic models. The starting point is the simulation of plant production for a given location depending on the environmental conditions and the inputs for agriculture. In order to make these simulations dependent on ecological functions and incorporate multitrophic interactions, suitable scaling parameters are required that consider landscape structure and composition. Homogeneous spatial units providing the input parameters for yield simulations are usually derived from spatially referenced intersection data on environmental conditions. For the analytical framework to implement BPM, these scaling parameters must additionally consider landscape structure and composition as well as negative externalities due to input intensity, which ensures the interdependence between biodiversity and yields in the yield estimates. This allows a nuanced quantitative assessment of the effects of changes in landscape parameters or input intensity on crop yields and biodiversity. Established model systems, like LPJml [49.Schaphoff S. et al.LPJmL4 – a dynamic global vegetation model with managed land – part 1: model description.Geosci. Model Dev. 2018; 11: 1343-1375Crossref Scopus (51) Google Scholar], InVEST (http://naturalcapitalproject.stanford.edu/software/invest), or SWAT (http://swat.tamu.edu), could serve as testbeds for implementing our analytical framework.Box 1Analytic Framework Deciphering the BPMAn agro-ecological and -economic framework that comprehensively accounts for the most relevant land and non-land inputs, including biodiversity, starts with underlying crop growth processes. Figure IA illustrates how growth of individual crops depends on environmental parameters E, such as available water, nutrients, and soil fertility, but also anthropogenic inputs, such as labor, irrigation, and fertilizer. This is illustrated by a differential equation estimating crop growth Y dynamically. Crop growth could be implemented in more complex ways, that is, by distinguishing different plant organs, or in more aggregated ways, such as using regression models that do not account for intra-annual dynamics. To upscale such models to larger regions or even to the global level, such models are often repeatedly run with changing model parameters depending on the location x and by using spatial maps that supply data on cropland extent, environmental conditions E, and anthropogenic input A (Figure IC).Incorporating multitrophic interactions of crop growth with aboveground biodiversity in such an approach is challenging, as biodiversity changes are driven beyond the point scale. A crop growth model that fully accounts for the BPM requires incorporating landscape-scale properties, which are the relevant drivers of biodiversity, such as composition and configuration of the landscape as well as input intensity [63.Graham L.J. et al.Incorporating fine-scale environmental heterogeneity into broad-extent models.Methods Ecol. Evol. 2019; 10: 767-778Crossref PubMed Scopus (13) Google Scholar]. While information on input intensity LI is often available on a grid scale, such as fertilizer F, irrigation I, or labor L, the quantification of landscape composition and structure can be assessed by landscape metrics (e.g., [52.Dominik C. et al.Landscape composition, configuration, and trophic interactions shape arthropod communities in rice agroecosystems.J. Appl. Ecol. 2018; 55: 2461-2472Crossref Scopus (30) Google Scholar]). A specific multitrophic interaction (e.g., pollination, biocontrol) defines the radius around a field in which landscape configuration, composition, as well as input intensity matters for the relevant biodiversity metrics (BD), such as presence, absence, or abundance of important species traits that provide pollination or biocontrol services. For example, insect-based pollination landscape metrics can be calculated for a radius of 250 m, 750 m, or 1 km around fields, distances of up to 3 km can be relevant for both pollination and biocontrol-providing organisms [64.Steffan-Dewenter I. et al.Pollination, seed set and seed predation on a landscape scale.Proc. R. Soc. B Biol. Sci. 2001; 268: 1685-1690Crossref PubMed Scopus (165) Google Scholar, 65.Steffan-Dewenter I. et al.Scale-dependent effects of landscape context on three pollinator guilds.Ecology. 2002; 83: 1421-1432Crossref Scopus (820) Google Scholar, 66.Hass A.L. et al.Landscape configurational heterogeneity by small-scale agriculture, not crop diversity, maintains pollinators and plant reproduction in Western Europe.Proc. R. Soc. B Biol. Sci. 2018; 285: 20172242Crossref PubMed Scopus (95) Google Scholar].Biodiversity data on species' presence or abundance in turn alters the crop growth process, either by promoting or reducing growth. Besides the well-established functions, which modify a maximum growth rate rmax given available water or nutrients, this maximum growth rate rmax can be adapted based on multitrophic interactions (Figure IC). An agro-ecological and -economic framework that comprehensively accounts for the most relevant land and non-land inputs, including biodiversity, starts with underlying crop growth processes. Figure IA illustrates how growth of individual crops depends on environmental parameters E, such as available water, nutrients, and soil fertility, but also anthropogenic inputs, such as labor, irrigation, and fertilizer. This is illustrated by a differential equation estimating crop growth Y dynamically. Crop growth could be implemented in more complex ways, that is, by distinguishing different plant organs, or in more aggregated ways, such as using regression models that do not account for intra-annual dynamics. To upscale such models to larger regions or even to the global level, such models are often repeatedly run with changing model parameters depending on the location x and by using spatial maps that supply data on cropland extent, environmental conditions E, and anthropogenic input A (Figure IC). Incorporating multitrophic interactions of crop growth with aboveground biodiversity in such an approach is challenging, as biodiversity changes are driven beyond the point scale. A crop growth model that fully accounts for the BPM requires incorporating landscape-scale properties, which are the relevant drivers of biodiversity, such as composition and configuration of the landscape as well as input intensity [63.Graham L.J. et al.Incorporating fine-scale environmental heterogeneity into broad-extent models.Methods Ecol. Evol. 2019; 10: 767-778Crossref PubMed Scopus (13) Google Scholar]. While information on input intensity LI is often available on a grid scale, such as fertilizer F, irrigation I, or labor L, the quantification of landscape composition and structure can be assessed by landscape metrics (e.g., [52.Dominik C. et al.Landscape composition, configuration, and trophic interactions shape arthropod communities in rice agroecosystems.J. Appl. Ecol. 2018; 55: 2461-2472Crossref Scopus (30) Google Scholar]). A specific multitrophic interaction (e.g., pollination, biocontrol) defines the radius around a field in which landscape configuration, composition, as well as input intensity matters for the relevant biodiversity metrics (BD), such as presence, absence, or abundance of important species traits that provide pollination or biocontrol services. For example, insect-based pollination landscape metrics can be calculated for a radius of 250 m, 750 m, or 1 km around fields, distances of up to 3 km can be relevant for both pollination and biocontrol-providing organisms [64.Steffan-Dewenter I. et al.Pollination, seed set and seed predation on a landscape scale.Proc. R. Soc. B Biol. Sci. 2001; 268: 1685-1690Crossref PubMed Scopus (165) Google Scholar, 65.Steffan-Dewenter I. et al.Scale-dependent effects of landscape context on three pollinator guilds.Ecology. 2002; 83: 1421-1432Crossref Scopus (820) Google Scholar, 66.Hass A.L. et al.Landscape configurational heterogeneity by small-scale agriculture, not crop diversity, maintains pollinators and plant reproduction in Western Europe.Proc. R. Soc. B Biol. Sci. 2018; 285: 20172242Crossref PubMed Scopus (95) Google Scholar]. Biodiversity data on species' presence or abundance in turn alters the crop growth process, either by promoting or reducing growth. Besides the well-established functions, which modify a maximum growth rate rmax given available water or nutrients, this maximum growth rate rmax can be adapted based on multitrophic interactions (Figure IC). It is estimated that in 23% of cultivated terrestrial landscapes yields are declining, most likely due to land degradation, lack of ecosystem functionality, and declining biodiversity [9.Díaz S. et al.Summary for Policymakers of the Global Assessment Report on Biodiversity and Ec