Agricultural intensification and land use change: assessing country-level induced intensification, land sparing and rebound effect

As populations grow in size and affluence, their demands for land-based products and services increase. Crop production can be augmented by allocating more land to crop production (cropland expansion) or by producing more crops per unit of cropland (cropland intensification) (Thomson et al 2019, Meyfroidt et al 2018, Yao et al 2018). Both options create socio-environmental tradeoffs, including the loss of grassland and forest (Curtis et al 2018, Spawn et al 2019). Agricultural intensification, through increases in land productivity—i.e. crop yields—or in total factor productivity (TFP) (Coomes et al 2019), may reduce pressure on land through the so-called Borlaug hypothesis of land sparing. This hypothesis postulates that intensification, by fulfilling a given demand for land-based products with a smaller land base, releases land for other uses including nature preservation (Meyfroidt et al 2018). Modeling studies suggested that the intensification of staple crops globally during the Green Revolution from 1965 to 2004 resulted in relative land sparing—i.e. reducing per-capita land demand or the rate of agricultural expansion compared to the counterfactual scenario without intensification (Stevenson et al 2013).

However, the relationship between agricultural intensification, demand for agricultural products and land use is complex. An alternative hypothesis builds on the 'Jevon's paradox' or 'rebound effect' (Alcott 2005). The paradox occurs if an increase in the productivity of one factor (here cropland) leads to its increased utilization, in a form of spillover where adoption of intensifying practices increases agricultural profitability and stimulates land-use expansion (Angelsen and Kaimowitz 1999; Lambin and Meyfroidt 2011, Hertel 2012, Byerlee et al 2014, Meyfroidt et al 2018).

Three main factors have been hypothesized to condition whether agricultural intensification in one country may lead to land sparing or a rebound effect domestically (Rudel et al 2009, Lambin and Meyfroidt 2011, Byerlee et al 2014, Hertel et al 2014, Villoria et al 2014, Meyfroidt et al 2018): (i) land-supply elasticity, (ii) price elasticity of demand of agricultural products and (iii) competitiveness of the intensifying region. First, land-supply elasticity refers to biophysical, technical (e.g. lack of accessibility in some regions) or institutional (land-use policies, tenure or access rules) constraints on cropland expansion, which are expected to promote land sparing. Weak constraints would support a rebound effect. Over 1970–2005, paired increases in yields and declines in cropland for ten major crops were mostly observed in countries with strong land use policies (Rudel et al 2009). Second, a rebound effect is more likely when intensification results from a technological change shifting the supply curve, and the demand is elastic to prices. This is more likely for products such as meat, luxury or leisure crops (e.g. cocoa, coffee), feed or bioenergy crops, or when markets are well integrated and the intensifying region is large enough to influence prices. With inelastic demand to prices, which is particularly the case for staple crops and when markets are closed, price-reducing intensification will trigger less reactions on the demand and may result in land sparing. Statistical approaches have found evidence supporting this hypothesis for staple crops in developing countries, but non-staple crops have expanded onto lands spared due to the intensification of staple crops (Ewers et al 2009). Third, when intensification increases the ability and performance of a set of producers to sell a product in a given market compared to competitors—i.e. competitiveness—, a local rebound effect is likely either because consumers substitute other products by the more competitive one, or because the region gains market shares against competitors abroad. In the latter case, increased competitiveness may spare land in other regions especially if the product demand is inelastic (Villoria 2019a, 2019b).

Conversely, changes in cropland area are also expected to influence intensification. Induced intensification theories, building on (Boserup's 1965) insights, suggest that intensification arises as a response to growing demand—due to increases in population density, local consumption patterns, and connections with distant markets—along with scarcity of productive and accessible land for agricultural expansion (Turner and Ali 1996). This demand is moderated and mediated by availability of technologies and institutional constraints on land-use expansion among others. Over the long term, technological innovation can endogenously respond to changing scarcity of production factors, as agents invest in innovations that enhance labor or land productivity, determined by the scarcity of either (Ruttan and Hayami 1984, Ruttan 1997). Theoretically, induced intensification is compatible with both land sparing and rebound effect, although in practice, intensification induced by land scarcity and population pressure is unlikely to cause a strong rebound effect.

Our objective is to test the three hypotheses of land sparing, rebound effect and induced intensification by exploring the empirical relationships between changes in cropland intensity and extent, at country scale (figure 1). We assess: (i) What are the effects of changes in cropland area on agricultural intensification, measured through yields and total factor productivity? and (ii) What are the effects of change in cropland intensity on the expansion and contraction of cropland area?

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We used an econometric approach with a dataset covering 161 countries over the 1961–2016 period—i.e. a panel—, with data on cropland area and productivity, and key control variables (table S1 (available online at stacks.iop.org/ERL/15/085007/mmedia)). Previous works have explored these questions using general and partial equilibrium models to simulate episodes of agricultural intensification and their effects on cropland (Stevenson et al 2013, Hertel et al 2014). Others works used econometric analysis of country-level panels (Ewers et al 2009, Rudel et al 2009, Ceddia et al 2013, 2014). Here we make progress towards addressing methodological issues in this literature: (i) The use of yield growth as a single measure of intensification, (ii) endogeneity between technological change, factor substitution and land use on the long run, possibly related to simultaneity bias, and (iii) corroborating casual inference via causality tests (Byerlee et al 2014, Villoria et al 2014). Recent efforts explored the role of TFP and used instrumental variables and ordinary least squares (OLS) to explore the potential effects of simultaneity bias and other sources of endogeneity between TFP and cropland area changes (Villoria 2019a, 2019b).

Our study addresses previous methodological issues by using (i) TFP—i.e. the efficiency of the overall mix of production factors (land, labor, and capital) due to improved technologies, farmer's skills, and knowledge (Coelli and Rao 2005, Fuglie 2015, Coomes et al 2019)—along with yield, as two distinct measures of intensification, (ii) lagged variables and first difference estimators to address possible endogeneity, (iii) cointegration to disentangle long- and short-run relationships and identify correlations due to third variables—i.e. spurious relations (Granger 1981), and (iv) an approach designed by (Canning and Pedroni 2008) to test for the direction of the causal effects in our long-run estimated coefficients. This allowed obtaining unbiased short-run effects and exploring the long-run equilibrium and relationships between these variables. Our national-level focus allows exploring the broad patterns across different regions and different types of crops, using long-term data on units large enough to internalize some of the spillover effects.

2.1. Data and variables

2.2. Hypotheses tested

2.3. Time series properties of the data

2.4. Specification of the dynamic error correction model

Our time series were individually non-stationary and cointegrated, and could thus be represented with a dynamic error correction model (ECM) (Engle and Granger 1987). We estimated this model using the Engle and Granger two-step procedure. First, we estimated the long-run cointegrating relationship (equations (1)–(2)). Second, we specified the short-run equations (equations (3)–(4)) by introducing the residuals of the long-run regression (${\mu _{1,i,t}}$), called the error correction term (ECT). The ECT indicates the degree to which the last period's deviation from a long-run equilibrium influences short-run dynamics. The ECT's coefficients (${\gamma_7},\,\gamma^{\prime}_{7}$) are comprised between [−1; 0] and adjusts the variables towards the equilibrium keeping the long-run relationship intact, with a larger value indicating a more rapid adjustment (Canning and Pedroni 2008). The ECM allows obtaining unbiased estimates of the short-run effects of the explanatory variables.

We explored different model specifications including a range of explanatory variables (table S6), and selected a reduced set of variables, with GDP per capita, rural population density, and average temperature and precipitation as control variables, allowing to include a wide range of countries and a long period (equations (1)–(4)). Yield and cropland area are the dependent variables measuring cropland intensification and expansion respectively. To check the model's robustness, we replicated our analyses without TFP, producing similar results for most variables and subsets (tables S11–S14).

In these equations, i represents the country (cross sectional component), t the time series and c the specific crop or crop group. We used a model with country fixed effects for both long- and short-run equations—controlling for the unobserved heterogeneity between countries—and dummy variables for each year in the long-run equations to control for time fixed effects such as global inputs and outputs prices fluctuations or other uncontrolled weather conditions. As domestic prices mediate the relationship between changes in cropland area and intensity, we did not include producer price indices as control variables (further explanation in SI Text).

We specified short-run models by using first-differences and lagging the variables by one period to avoid endogeneity, such as between TFP and yields (equations (3)–(4)).

2.4.1. Long-run model specifications

$$\begin{equation}\begin{gathered} LogYiel{d_{i,c,t}} = \,{ \propto _{1i}} + {\beta _1}LogCroplan{d_{i,c,t}} + {\beta _2}TF{P_{i,t}}\\ \ + {\beta _3}LogGDPcapit{a_{i,t}} \\ + {\beta _4}LogRPDensit{y_{i,t}}\\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ + {\beta _5}AvgRai{n_{i,t}}\hfill\\ \ \ \ \ + {\beta _6}AvgTemperatur{e_{i,t}} \\ \ \ \ \ \ \ \ \ \ + {\beta _7}Timedumm{y_t} + {\mu _{1,i,t}} \\ \end{gathered} \end{equation} \tag{ 1 }$$

$$\begin{equation}\begin{gathered} LogCropland{_{i,c,t}} = \,{ \propto _{2i}} + {\beta^{\prime} _1}LogYield{_{i,c,t}} + {\beta^{\prime}_2}TF{P_{i,t}}\\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ + {\beta^{\prime}_3}LogGDPcapit{a_{i,t}} \hfill \\ \ \ \ \ \ \ \ \ \ \ \ \ + {\beta^{\prime}_4}LogRPDensit{y_{i,t}}\\ \ \ \ + {\beta^{\prime} _5}AvgRai{n_{i,t}}\\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ + {\beta^{\prime}_6}AvgTemperatur{e_{i,t}} \hfill \\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ + {\beta^{\prime}_7}Timedumm{y_t} + {\mu _{2,i,t}} \hfill \\ \end{gathered} \end{equation} \tag{ 2 }$$

For all equations:

$$\begin{align*}& i\epsilon\left\{ {1,2, \ldots .,N} \right\}\!,t\epsilon\left\{ {1,2, \ldots .,{T_i}} \right\},\\ & c\epsilon\left\{ {1,2, \ldots .,{C_i}} \right\}\!,\,{\mu _{i,c,t}}\left( {0,\sigma _\mu ^2} \right)\end{align*}$$

2.4.2. Short-run model specifications

$$\begin{align}\Delta LogYiel{d_{i,c,t}} &= {\gamma _1}\Delta LogCroplan{d_{i,c,t- 1}}\notag\\ & \ \ \ \ \ + {\gamma _2}\Delta TF{P_{i,t - 1}}\notag\\ & \ \ \ \ \ + {\gamma _3}\Delta LogGDPcapit{a_{i,t - 1}}\notag\\ &\ \ \ \ \ + {\gamma _4}\Delta LogRPDensit{y_{i,t - 1}}\notag\\ &\ \ \ \ \ + {\gamma _5}\Delta AvgRai{n_{i,t - 1}}\notag\\ &\ \ \ \ \ + {\gamma _6}\Delta AvgTemperatur{e_{i,t - 1}}\notag\\ & \ \ \ \ \ \, + {\gamma _7}\hat{\mu}{_{i,t - 1}} + {\varepsilon _{i,t}}\end{align} \tag{ 3 }$$

$$\begin{align}\Delta LogCroplan{d_{i,c,t}} & = {\gamma ^\prime_1}\Delta LogYiel{d_{i,c,t - 1}}\notag\\ &\ \ \ \ \ + {\gamma ^\prime_2}\Delta TF{P_{i,t - 1}}\notag\\ &\ \ \ \ \ + {\gamma ^\prime_3}\Delta LogGDPcapit{a_{i,t - 1}}\notag\\ &\ \ \ \ \ + {\gamma ^\prime_4}\Delta LogRPDensit{y_{i,t - 1}}\notag\\ &\ \ \ \ \ + {\gamma ^\prime_5}\Delta AvgRai{n_{i,t - 1}}\notag\\ &\ \ \ \ \ + {\gamma ^\prime_6}\Delta AvgTemperatur{e_{i,t - 1}} \notag\\ &\ \ \ \ \ + {\gamma ^\prime_7}\hat{\mu}{_{i,t - 1}} + {\varepsilon _{i,t}}\end{align} \tag{ 4 }$$

For all equations:

$$\begin{align*}& i\epsilon\left\{ {1,2, \ldots .,N} \right\}\!,t\epsilon\left\{ {1,2, \ldots .,{T_i}} \right\},\\ & c\epsilon\left\{ {1,2, \ldots .,{C_i}} \right\}\!,\,{\varepsilon _{i,c,t}}\left( {0,\sigma _\varepsilon ^2} \right)\end{align*}$$

2.5. Long-run causality test

3.1. Panel unit root tests and cointegration

3.2. All crops, by income groups (figures 2, 3, tables S7, S9)

The coefficients of the long-run regressions are unbiased and thus can be interpreted for their respective samples, but the test for the sign of the causality was significant for only some of those long-run coefficients. TFP had a positive and strong impact on yields for middle and high-income countries in the long run and for middle-income countries in the short run (figure 2, table S7). Cropland area had a negative effect on yields over the long run within our sample, which covers most countries of the globe over a meaningful time period, but the causality test failed to show statistically significant result beyond the sample. For all countries together, TFP had a positive effect on yields and a negative one on GDP. Over the long run, TFP had a positive effect on cropland area for all countries together, and for low-income countries, TFP had a positive effect and rural population density a negative effect (figure 3, table S9).

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All short-run regressions confirmed a long-run equilibrium between the variables, with the variable 'residuals', corresponding to the ECT, being significant and between 0 and −1. Farmers adjusted yields towards the long-run equilibrium generally more rapidly than cropland area, as indicated by the higher ECTs for yield as dependent variable compared to cropland area. Cropland area had a negative effect on yields in high-income countries and positive effect for middle-income countries (figure 2, table S7). TFP had a positive impact in low, high-income countries and all countries together, while rural population density had a negative effect in middle-income countries and all countries together. For cropland area (figure 3, table S9), yield had a positive but weak impact in middle-income and all countries together. TFP also had a positive and stronger impact in low- and middle-income countries, as well as all countries together. Rural population density and GDP had positive impacts on cropland area in low, middle-income and all countries together, and low-income countries and all countries together, respectively.

3.3. Crop- and region-specific results on yields (figure 2, tables S7, S8)

3.4. Crop and region-specific results on cropland area (figure 3, tables S9, S10)

The induced intensification theory supporting our Hypothesis 1 has a solid body of local evidence (Turner et al 1993, Turner and Ali 1996, Meyfroidt et al 2018). In our results, aggregating all crops, we found significant evidence for intensification induced by restrictions on cropland expansion, be they biophysical, technological, economic or institutional, only in the short run in high-income countries. Over the long run, though, our unbiased coefficient support the hypothesis for all our income groups for our sample of countries and years, which represent a crucial period of intensification such as through the Green Revolution (Stevenson et al 2013, Pellegrini and Fernández 2018). Increased population pressure caused cropland expansion in the short run in low and middle-income countries, but the opposite over the long run in low-income countries. This suggests that although the short-term response of increasing population pressure is generally to expand cropland, over the long-run induced intensification may reduce cropland expansion in low-income countries. We also found support for our Hypothesis 1 for staple crops such as cereals and oil palm in Africa, as well as for labor-intensive crops such as cocoa and coffee in the short run. Staple crops respond more directly to local demand than non-staples such as high value crops, and thus are more likely to see their production increasing through intensification if expansion is constrained.

In contrast, for several key commodity crops, cropland expansion induced an increase in yields over the long run (cocoa, sugarcane, oil palm, rubber, the group of high-elasticity crops and flex crops) or the short run (whole cropland area in middle-income countries, staple crops in SSA, soybean, banana). This could suggest the presence of agglomeration economies for these crops. When a country progressively expands the production of these crops, sometimes by specializing into them, producers may benefit from positive externalities such as increased knowledge transmission, skilled labor force, access to cheaper inputs, policy support, and others, which allow for yield improvements, as shown for soybean in Brazil (Garrett et al 2018, Richards 2018) and banana and pineapple in Costa Rica (Shaver et al 2015, Jadin et al 2016). Induced intensification and agglomeration economies are not mutually exclusive intensification pathways, but our results suggest that in some circumstances one dominates. Despite the lack of a strong induced intensification effect in our dataset for some key commodities, local studies provide evidence that restrictions on land use expansion can indeed induce intensification even for high-demand commodities, as shown in the Brazilian Amazon for soy and beef (Garrett et al 2018, Koch et al 2019).

We tested conditions under which intensification results in land sparing or rebound effect. Our Hypothesis 2 differentiated crops depending on their price-elasticity of demand. At the crop-specific level, we found evidence of land sparing over the long run for some staple cereals: With increasing yields for rice as well as for wheat, rice and maize together, and increasing TFP for wheat, and over the short term for wheat with TFP and yields. Among major crops, wheat and rice were shown to have lower growing-area elasticities to price than corn and soybeans (Iqbal and Babcock 2018). Land sparing with intensification of these crops is thus expected (Stevenson et al 2013, Phalan et al 2014). Yet, even for these crops, low absolute values in the short-term effects of yields increase indicates a partial rebound effect that reduces the land sparing.

Other subsets showed evidence of a rebound effect, mostly for low and middle-income countries. For all crops aggregated, yields had a rebound effect in middle-income countries over the short run. Many countries in this group, such as in Latin America, have export-oriented agriculture strongly engaged in global commodity markets, and land available for cropland expansion. Intensification increased their competitive advantage and supported increasing exports as well as growing domestic consumption with economic growth (Yao et al 2018). Crop-specific results showed a rebound effect of yields or TFP increases for several commodities with high price-elasticity of demand in which middle-income countries are dominant players, including bananas, coffee, sugarcane, rubber, flex crops, tropical fruits, palm oil in Africa, soybean in South American frontier countries, and the group of high-elasticity crops in tropical countries in the short run. Villoria (2019a) also found a rebound effect for countries with large commodity exporting sectors. Over the long run, the evidence for rebound effects is less strong, still existing for some crops such as rubber and flex crops, but disappearing for most other crops and, aggregating all crops, remaining mainly for intensification through technological progress, and only significant in low-income countries. Over the long run, and with economic development, rebound effects may decline as demand for many products saturate, or with stronger constraints on cropland expansion.

Regarding our Hypothesis 3, for low-income countries, TFP improvements induced a rebound-effect over both the long and the short run, corresponding to theoretical expectations for countries with poorly competitive agriculture, in which increasing TFP and competitiveness may decrease reliance on imports (Hertel et al 2014). This result was supported by the short-run rebound effect of yield increases on staple crops area in Sub-Saharan Africa. In contrast, the lack of effect of TFP on cropland area in high-income countries could be related to the much stronger constraints on land use expansion in these countries (Rudel et al 2009, Lambin and Meyfroidt 2011). There, technological progress mostly affects the efficiency of other inputs, in particular labor, rather than land. In the short run, neither the land sparing or rebound effect hypotheses were validated for high-income countries, indicating that other forces such as land use zoning and agricultural policies including subsidies may have stronger effects on cropland area changes. Increases in TFP had a positive impact on yields in many subsets, confirming the importance of continuing agricultural research as a strategy to increase yields (Coomes et al 2019).

Our analyses measure rebound-effect and land sparing as the effect of changes in one variable on another one—i.e. in relative terms—, but not directly indicating absolute cropland expansion or contraction. Our aggregated analyses summing all crops together allow investigating land sparing and rebound through substitution among crops, but do not account for substitution with other land uses (pasture, forestry). In our crop-specific analyses, the rebound and land sparing effect may represent effects on the total cropland area or substitution for one crop to another. Area expansion of a particular crop can come from land cultivated with other crops, from other land uses such as pastures, or from new conversion of land to agriculture. Thus, we cannot directly draw conclusions regarding net effects on deforestation or other natural habitat clearing. A rebound effect of cropland expansion occurring on pastures may still result in net contraction and land sparing on total agricultural lands, if crops produced intensively substitute for grass as animal feed (Yao et al 2018). Further, we only examine the rebound or land sparing within the region that experiences intensification, and thus cannot measure if land use changes in one country have land sparing or rebound effects elsewhere (Villoria 2019a, 2019b). Finally, the crop-specific analyses used harvested areas of these crops as a proxy for cropland area. Increasing harvest frequency was thus accounted for as area expansion, which likely blurred the results. This is particularly relevant for rice, the main crop for which several harvests per year are grown over significant areas (Portmann et al 2010), and to a lesser extent for soy which is increasingly double cropped (Spera et al 2014). Thereby we also could not adequately capture intensification occurring through shortening of fallows. We kept all variables together in one long-run relationship. Further works could also explore these questions by separating the long-run relationships among variables into several equations, with a different estimation of the ECM (Johansen 1988).

Human societies face trade-offs between production-oriented land uses and ecological outcomes (Phalan et al 2014, Phalan 2018, Meyfroidt et al 2018). Globalized food systems can contribute to a more efficient use of agriculture globally (Egli et al 2018, Heck et al 2018) but can also trigger increased consumption of natural resources (Bruckner et al 2012, Barger et al 2018, Tramberend et al 2019). Agricultural intensification is often seen as a key tool for sustainability, to lessen competition for productive land and mitigate associated trades-offs, but the dynamics and spillover effects of intensification remain insufficiently understood (Meyfroidt et al 2018).

Here, we developed and applied a methodological design that addresses key issues from previous statistical approaches, by analyzing different dimensions of intensification, and assessing long- and short-run mutual causal relationships between cropland area and intensity. We provided empirical evidence that the presence of a land-sparing versus rebound effect of intensification, as well as of induced intensification, varied across subsets of crops with distinct price elasticity of demand and labor intensity, and across regions with different conditions in terms of demand elasticity, constraints on cropland expansion, competitiveness and openness to markets. Our results support the induced intensification hypothesis for low-income countries over the long run to some extent, as well as for staple crops and oil palm in Sub-Saharan Africa, and labor-intensive crops such as cocoa and coffee in the short run. Intensification resulted in a rebound effect over the short run in low and middle-income countries, the latter including many key agricultural producers with strong competitiveness in global agricultural commodity markets, and land available for cropland expansion. This rebound effect manifested over the short run for several commodities with high price-elasticity of demand, including bananas, sugarcane, rubber, flex crops, tropical fruits, palm oil in Africa and soybean in Latin America frontier countries. Over the long run, 'backfire' rebound effects remained for some key high-elasticity commodities such as flex crops and rubber for their main producer countries, and soybean within our sample. Intensification of staple cereals such as wheat and rice, as well as total cropland area in middle-income countries, resulted in significant land sparing. Lastly, intensification driven by technological progress was generally more likely to result in a rebound effect than intensification due to yields increases driven by increased inputs use.

Our approach allows distinguishing short-term from long-term effects, which is key as crucial socio-environmental impacts occur when gross natural habitat conversion occurs. These insights can contribute to improve land-use policies targeted to specific contexts and crops, confirming that apart for staple crops over the long run, intensification is unlikely to result in actual land sparing for nature unless accompanied by appropriate policies and other interventions to control and orient cropland expansion (Lambin et al 2014). Further analyses could replicate our study with distinct sets of crops, countries and control variables, as well as with subnational-level data, to test for additional conditions and further refine theories explored. Our study design, building on cointegration, error-correction models and tests for long-run causality direction could be used for addressing other complex long- and short-run causal dynamics in land and social-ecological systems.

The authors thank participants in the Land Use and Leakage workshop at the Robert Bosch Foundation in Berlin, November 9–10, 2017 for valuable comments during preliminary stages of this paper. This work has received support from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant agreement N° 677140 MIDLAND, https://erc-midland.earth). It also contributes to the Global Land Programme (https://glp.earth).

The data that support the findings of this study will be openly available following a delay from publication using the following unique identifier https://doi.org/10.5061/dryad.9zw3r22b8.