Climate change induced transformations of agricultural systems: insights from a global model

We use simulations from the EPIC GGCM [25, 26] to estimate yield, input level, and cost coefficients for 17 crops and 4 crop management systems spanning main input intensity gradients (rain-fed and irrigated high fertilization, rain-fed low fertilization, and subsistence farming) in the present climate and nine climate change scenarios. These scenarios span major uncertainties along the impact assessment chain along one central scenario (table 1), including anthropogenic perturbation of the climate system using four representative concentration pathways RCPs [27]; the response of the climate system using five GCMs selected to capture major uncertainties in the climate response [28]; and a scenario without the effects of increased atmospheric carbon dioxide concentration (CO₂) on plants, as a pessimistic boundary of this major uncertainty (SOM section 1.1.2). Simulations were performed in the frame of the Inter-Sectoral Impact Model Inter-comparison Project [10, 28], over more than 200 000 pixels of potential cropland on a regular 0.5° latitude–longitude grid intersected with classes of homogeneous soil, altitude, and slope and country boundaries. We re-aggregated the simulated values from 0.5° to 2° and implemented them into GLOBIOM (SOM section 1.2).

We model adaptations in the agricultural sector with GLOBIOM [29], a bottom-up global recursive dynamic partial equilibrium model of the agricultural, bio-energy and forestry sectors. Its simultaneous modelling at global scale of both regional market interactions and bottom-up evolution of production systems makes it well suited among other global models for assessing climate change induced changes to production systems. It determines prices, production, consumption and bilateral trade flows endogenously for 30 aggregated regions over 10 years time steps, and we therefore hereafter refer to time horizons as decades (e.g., '2050s' to be understood as 'the decade from 2050 on'). Demand, trade and market equilibrium are modelled at regional level, while production is modelled at the scale of agricultural production systems, defined at the above mentioned spatial resolution by combination of activities and management systems. Market prices adjust so that total production equals demands for food, feed, and energy. The 'Middle of the Road' shared socio-economic pathway SSP2⁵ . Reference [30] is used to generate the baseline simulation including demand behaviour and production possibilities (e.g., technological progress) from 2000s to 2050s under present climate.

We define adaptations to climate change scenarios as the changes in endogenous variables occurring under these scenarios relative to the baseline. This definition allows representing difference in decisions of agents due to the sole effect of changes in climate, and is consistent with IPCC definition of 'the process of adjustment to actual or expected climate and its effects' [31], as well as with common definitions in earlier studies using modelling tools (e.g., [2, 5]). At regional scale, consumers modify their consumption level by product, and alter their origin between regional production and imports from various regions. As summarized in table 2, at high spatial resolution producers adapt by altering field-scale crop management practices (as simulated by EPIC, see SOM section 1.1). They also alter existing cropland allocation to various crops and managements within limits set by maximal conversion rates per time step. This includes shifts from rainfed to irrigated production at high resolution with maximal expansion rates, while accounting for current regional water scarcity (SOM section 1.5). Producers can also alter the extent of cropland within the limits imposed by land use change costs, competition with other uses, a maximum per time-step conversion rate and regional land scarcity costs (SOM section 1.4). Overall, producers thus adapt to changes in local production possibilities and costs (affected by climate change through inputs from EPIC) and to market forces of demand and competition. Technological progress is exogenous, and we do not include adaptations such as introduction of crops not initially present in a spatial unit, changes in cultivars, or endogenous directed technological progress. We do not account for future changes to the availability of water resource for irrigation purposes.

There is no clear operational definition of transformational adaptations in agriculture beyond the idea of significant change either to the rationale for using land within system or to the location of a system to pursue the same goal [15, 16]. Criteria include the depth, generality, extent or permanence of changes within a system, or of impacts outside the system [15], but also pro-activeness, knowledge and financial investment as well as lifetime of adaptations [23, 32, 33]. Examples range from land acquisition in a remote location by producers in Australia either in reaction or in anticipation of changes in climate [34], to the historical paradigm shifts in farming activities in developed countries throughout the 20th century [16]. Our modelling framework cannot diagnose endogenously paradigm shifts in the use of cropland by producers since they are assumed to always react to on-going changes in local biophysical (e.g., yield) and market conditions to maximize profits. It is better suited to investigate the notion of transformation based on large changes to the use of land and water resource. To separate classes of agricultural systems adaptations in our model, we compiled qualitative and quantitative estimates regarding their lifetime, irreversibility, and knowledge and financial investment requirements (table 2) from various sources [23, 32, 33], with a particular focus on changes in the use of land and water resources.

One class consists of incremental adaptations, including adjustments in crop management practices (altering crop calendar or nutrient and water input levels), or in cropland allocation among existing crops. These are tactical choices requiring minimal financial investment, few cropping seasons for the mastery of associated managerial skills, and can be reversed from one cropping season to another. They can be routinely implemented by farmers in anticipation of changes in yields and prices over short time periods [12, 35–38].

Adaptations become systemic and then transformational in proportion to their irreversibility, capital requirements, lifetime, and impact. For example, shifts from rainfed to irrigated systems are systemic due to estimated lifetimes of 20–30 years and significant capital requirements (table 2). Irrigation expansion becomes transformational when undertaken on a scale which exacerbates competition for water resources, affecting other users and potentially prompting large additional investments in long-lived water storage and distribution infrastructures (table 2). Similarly, we use the density of agricultural areas to trace transformational change because of underlying significant level of irreversibility and investments assumed in the literature (table 2). Expensive investment into transport and processing infrastructure would be required to build production capacity in new areas, while losses in the production capacity of established (i.e., dense) cropland areas may be lost over long time due to workforce migration and infrastructure degradation. For each time step we classified pixels into either agricultural or non-agricultural zones within each region: we compute in each pixel the cropland density as the share of cropland over total land available in the baseline scenario. In each region, pixels which fall above (respectively below) the third quartile in cropland density are classified as part of agricultural (respectively non-agricultural) zones. This definition does not distinguish underlying reasons between biophysical (e.g., unsuitable land) or socio-economic (e.g., protection policies, low access to market) factors although GLOBIOM accounts for both (SOM section 1.4). We thus focus on three different transformational adaptations of agricultural systems in a region: large increase in irrigated area (>25%, assuming large investment and lifetime related to a change in water resource), large cropland increase (>20%) in non-agricultural zones (i.e., pixels whose share of cropland is relatively low compared to other pixels in the same region, and where a large investment need is assumed in order to increase sectoral production capacity) and large cropland reductions in agricultural zones (>10%, large irreversibility related to loss in sectoral production capacity).

Uncertainty in the magnitude and the direction of appropriate transformational change is problematic, as ex-post adjustment of decisions will often be financially and politically very costly [23]. In this context, any transformational adaptation is a risky choice and the hope of more accurate information in the future acts as an incentive to postpone it. From a decision-making perspective, it is crucial to know when transformations are required, but also whether they are robust across plausible scenarios, as well as the extent to which robustness could be improved by uncertainty reduction. To quantify the robustness of a transformational change over a set of scenarios, we propose a signal-to-noise type of scenario analysis. On the one hand, we define the signal as the largest magnitude of an adaptation (e.g., increase in irrigated area) across scenarios⁶ , and compare it to a threshold to indicate whether a transformational change is required or not. On the other hand, we define the noise as the range of the adaptation across all scenarios: a noise larger than the signal indicates non-robustness, i.e., either uncertainty in the need for transformational change or very large uncertainty in extent.

By varying the time horizon of the analysis (2020s, 2030s, 2040s and 2050s), we derive when the need for a transformation arises and whether this need is robust or not across scenarios. By varying the set of scenarios considered, we diagnose whether the need for a transformation could be more robust while assuming lower uncertainty, and whether one scenario dimension can be identified as crucial to reduce uncertainties in required transformations.

By mid-century, climate change will have large biophysical impact on crops yields, equivalent to a −18% to +3% change in global vegetal calorie supply compared to the baseline scenario (first column of figure 1). However, impact varies among regions, crops, and management systems, thereby providing opportunities for targeted adaptations. Firstly, biophysical impacts greatly differ at large scales: when the world is split in ten macro regions (SOM section 1.3), the impacts vary not only in magnitude but also in direction (except for one scenario, SOM section 2.1, table ST2). This large-scale spatial variability of the impacts of climate change is a robust finding in the literature [3, 5, 10, 39], and invites world-wide redistribution of production systems. Secondly, impacts vary across crops (from −24% for oil palm to +17% for barley) and management systems (from −7% for irrigated high input systems to −2% for rainfed low input systems, table ST3 in SOM). Such a range is also consistent with literature [3, 10, 40] and allows for adaptation through local changes in the specialization and management of agricultural systems.

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Several assumptions regarding underlying phenomena contribute to each climate scenario and each of these affects impact patterns—and by extension adaptation opportunities—differently. Increasing atmospheric carbon dioxide concentration, for example, increases plant light and water-use efficiencies [41], but crop models such as EPIC could be optimistic [42]. Assuming no CO₂ effects worsen biophysical impacts on yields by about 10% at global scale (figure 1), but do not alter much the ranking of impacts across regions, crops and crop management systems (section 2.1 of SOM). The range associated with climate responses (using alternative GCMs) on a global scale is similar (11%), but in this case the effects of each climate response vary greatly across regions, crops, and crop management systems (section 2.1 of SOM), probably due to a large disagreement on changes in rainfall patterns [43, 44]. Alternative emissions pathways have similar effects but with overall lower amplitude (section 2.1 of SOM). This might be particular to the 2050 time horizon; emission pathways are expected to dominate over longer time scales due to divergence in cumulative emissions [45–47]. Finally, not all macro regions show similar sensitivity of impacts to the various scenario dimensions: high latitude temperate regions show greater variability in biophysical impacts, while tropical to subtropical regions face more systematically negative impacts (section 2.1 of SOM).

After accounting for adaptations, global vegetal calorie supply is reduced by only 3% at worse (last column of figure 1). This is consistent with earlier estimates, which reported final climate change impacts on global food consumption of a few percent by 2050s, with however significant spatial variability [3, 5]. Several supply-side adaptations combine to mitigate the effects of climate change. Using a decomposition method (section 2.2 in SOM), we estimate that global-wide shifts in management systems compensate for negative yield impacts by an equivalent of 2–3% of baseline global vegetal supply (second bar from the left). Underlying changes in management are crop, region and scenario specific but global scale irrigation development plays a significant role (SOM section 2.3). Crop substitution and relocation of production systems further compensates by up to 8% of baseline global vegetal supply (third bar from the left), with about one third of simulated cropland reallocations occurring trans-regionally (section 2.2 of SOM). This highlights the important role of trade at facilitating production reallocations between regions [5, 48–50]. After these adaptations (and including CO₂ effects), climate change results in yield gains of up to +4% (third bar from the right), while global cropland is reduced by up to 4% (last bar).

The various scenario dimensions modulate adaptation portfolios. Negation of CO₂ effects (figure 1, black dots compared to grey bars) almost doubles gains from reallocations, with small effects on the share of land reallocated within and among macro regions (SOM section 2.2). Overall, the final yield effect is negative and new land needs to be put into cultivation while the impact on final supply is twice worse. Climate model assumptions have the highest impacts on reallocation effects (thick error bars in figure 1), but only weakly affect gains from crop management system switches. Emissions pathways (thin error bars in figure 1) have a similar effects over smaller ranges.

Market interactions are another important, if indirect, driver of adaptations, as has been reported in the literature [1, 2, 49–52]. For example, despite the negative yield impacts of the HadGEM2-ES climate model (RCP 8.5 with CO₂ effects) in Latin and Central America (LAM), final supply, agricultural area, and net exports grow in this scenario relative to the baseline (figure 2). This occurs in most scenarios, due to heightened competitiveness of producers in LAM over those in Northern America (NAM, see SOM sections 2.2 and 2.5). Such effects are highly region-specific: the Middle East and Northern African regions (MNA) almost always become more competitive, while comparative advantage tends to decrease in the Commonwealth of Independent States (CIS) and Oceanic (OCE) regions. The effect on European regions (EUR) varies among scenarios without a clear trend. Sub-Saharan Africa (SSA) displays consistently weak adaptation due to world-high demand elasticity through 2050s as well as the predominance of subsistence production systems, for which reallocations are restricted. These regions contrast with Asian regions (EAS, SAS and SEA), where adaptation is facilitated by less elastic demand and larger ability to reallocate production systems internally. This illustrates a form of adaptive capacity implicitly embedded in region-specific model (e.g., demand, trade and land use change specifications), and socio-economic scenario (e.g., GDP and population trajectories) assumptions.

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Changes in the management, location and specialization of production systems translate at the scale of agricultural systems into different categories of adaptations. We illustrate with three individual countries how we distinguished those of a transformational nature (figure 3). Adjustment of cropland allocation among existing crops is of incremental nature. In the HadGEM2-ES/RCP 8.5 scenario (figure 3, 6–9th bars from the top in each panel), the area occupied by soybean increases by an equivalent of 7% of total cropland in Brazil at the expense of dry beans (−2%), rice (−2%), and other crops (−3%). In Mexico, sorghum partially supplants dry beans and corn, while in the USA corn replaces wheat and cotton.

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Larger changes to resource use imply transformational change for agricultural production systems. Relative to the baseline, cropland expands +26% in Mexico and decreases by 14% in the USA under the HadGEM2-ES (RCP8.5 with CO₂ effects) climate model by 2050s (figure 3, 2nd bar from the top). Cropland decreases by 13% in agricultural zones of the USA (figure 3), above the threshold defining transformational change and implying sectoral capacity loss not easily reversed. Similarly, for Mexico cropland increases in non-agricultural zones are three times greater than in agricultural zones (respectively +36% and +12%) and reaches a transformational extent implying significant infrastructural investments. The rationale for these changes lies in complex interactions between local biophysical properties, the opportunities offered by reallocations, and changes to comparative advantages across regions. For example, in the USA for this scenario the biophysical impacts are negative while the yield differential between agricultural and non-agricultural zones is halved (SOM section 2.5). Exports are cut and USA production capacity is reoriented towards domestic demand: remote climate change impacts induce in this scenario a loss in comparative advantage on international markets, while demand is rather inelastic. At the same time, Mexico reduces its net imports and increases its production capacity through cropland expansion into non-agricultural zones, but demand is strongly affected by competition with American consumers.

Lastly, irrigated areas increase in this scenario by 28%, 31%, and 33% by 2050s in respectively Mexico, the USA, and Brazil (figure 3, 5th bar from the top in each panel) where it is an important adaptation lever (SOM section 2.3). However, water use for irrigation increases by more than 20% in already water limited regions such as Mexico and the USA, where a doubled water scarcity cost limits irrigation expansion (section 2.4 in SOM).

Through scenario analysis, we diagnose where transformations are required, their robustness, as well as the factors shaping this robustness. For example, the maximum cropland decrease in agricultural zones across scenarios (figure 4(b)) is larger than −10%—and thus in the transformational range—in one third of regions by 2050s (red and green colours) but the amplitude of change is large in all these regions (red colour, non-robust) but CIS (green colour, robust). Similarly, in another third of regions, transformational cropland development (i.e., >+20%) in non-agricultural zones is expected although robust in only two regions (figure 4(c)). Most of the former cases are located in temperate latitudes of the Northern Hemisphere with significant uncertainty in yield impact (figure 4(a)), while the later are geographically scattered. Uncertain market indirect effects add to biophysical uncertainties (SOM section 2.2 table ST5): for example productivity gains in CIS or EUR do not necessarily translate to increased competitiveness due to sometimes limited export possibilities, leading to cropland losses. Similarly, the development of non-agricultural zones in Brazil under most (but not all) climate change scenarios despite robustly negative impacts (figure 4(a)) stems from non-robust opportunities of increased export to the USA (SOM section 2.2 table ST5). Overall, almost none of these transformations are robust across scenarios: this is also the case for large development of irrigation, the most wide-spread transformation. For about 70% of regions by 2050s, such a transformation could be relevant although not robust across all scenarios (figure 4(d)). This owes to the large uncertainties in future rainfall patterns and crop water-use efficiency, and would be worsen if we had accounted for the very uncertain projected changes to the availability of water resource for irrigation purposes [53] due to an increasing competition with other usage and climate change [54], and depletion of unsustainably used water resource [55].

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In such a context, knowing when transformations could be required, and their robustness over time are crucial information. Table 3 summarizes the information contained in figures 4(b)–(d) but repeated for various time horizons for all scenarios (ALL columns, see SOM section 3 for individual maps), the numbers indicating for each macro regions and time horizon (rows) and type of transformation (column groups) the number of regions within a macro region for which a transformation could be required (i.e., signal larger than threshold, either red or green colour in figure 4), and the number of these cases being robust across scenarios in parenthesis (green colour in figure 4). Transformational cropland decrease in agricultural zones could develop as early as 2020s in the USA and Southern and Eastern EUR where it would remain non-robust, whereas in other regions such a transformation would potentially occur only by 2040s, and would even be robust by 2050s for CIS. Similarly, developments of non-agricultural zones would not be required until 2040s for most concerned regions (except for Canada), but would generally remain non-robust across scenarios (except for Turkey by 2050s). On the contrary, transformational developments of irrigation could occur as early as 2020s in regions like Mid-Western Europe, Brazil, India, Australia or Southern Africa, and would quickly expand to Asian and Sub-Saharan regions from 2030s onwards. While the potential such transformation rapidly expands, it also remains largely non-robust across scenarios. In addition, columns ALL-{GCM}, ALL-{RCP} and ALL-{nCO₂} in table 3 present the same information as above, but if removing from the set of scenarios considered respectively GCMs other than HadGEM2-ES, RCPs others than RCP8.5, or the no-CO₂ scenario. For example, assuming the climate response would be much closer to that simulated by the HadGEM2-ES model than by other GCMs (ALL-{GCM} column) would remove the need for transformational increases in irrigated area by 2040s in five regions (from 17 to 12) and make it robust in three other regions (2–5). Two main results can be highlighted: first, uncertain reductions in a single scenario would only weakly reduce cases of non-robust need for cropland increase in non-agricultural zones, but could reduce up to two thirds of cases of non-robust need for transformational either increased irrigation or decreased cropland in agricultural zones. Secondly, over most time horizons and transformations, a more accurate climate response would best clarify the need for and robustness of transformations, as compared to other scenario dimensions.