Estimation of land productivity
Our analysis of farmland substitution costs is based on comparison of annual crop yield potential of converted and new croplands rather than on differences in current farm yields of both land categories. As noted in the main text of our paper, the latter approach can mask differences in the inherent productive capacity of agricultural land, as determined by soil quality and climate, due to differences in sophistication of crop and soil management practices or access to inputs and markets, all of which can limit yields31. In many developing countries, and especially at the frontiers of current agricultural areas, farmers have limited access to inputs, equipment, supporting services and technologies. However, we also evaluated substitution costs based on current average yields and the results are presented in Supplementary Table 2 although we believe these results are less useful. For example, substantial funding is allocated by government agencies and charitable foundations (e.g., Bill & Melinda Gates Foundation, CGIAR, USAID--Feed the Future Initiative) to improve farmer access to markets, technologies, and information in developing countries. Therefore, an analysis of land substitution costs to inform national policies concerning agricultural development and land use policies based on current yields would not only mask the potential cost of cropland substitution based on use of modern farming practices, but it would also quickly become outdated as farmers gain access to markets, technologies, and information.
Yield potential is the yield of a crop cultivar when grown with water and nutrients non-limiting and biotic stress effectively controlled11,32. Under these conditions, crop growth rate is determined by solar radiation, temperature, atmospheric CO2, and genetic traits that govern the length of growing period and light interception by the crop. For rainfed crops, rainfall amount and distribution and soil water holding capacity also impose an upper limit to crop productivity. Hence, yield potential is the most relevant parameter for estimating crop production potential of irrigated crop systems, while water-limited yield potential is the appropriate benchmark for rainfed crops. Current yield is defined as the yield achieved in farmer’s fields in recent years within a defined spatial unit.
We used crop models to estimate yield potential in each country. The main challenge to obtain accurate simulations using crop models is the availability of high-quality primary data for climate, soil, and crop management, which are the most sensitive parameters determining yield potential. Weather stations are sparse and soil and cropping system information is rarely adequate to estimate yield potential for many crop production areas in developing countries. To overcome that limitation, we used the Global Yield Gap Atlas26 (GYGA) “bottom-up” spatial framework that identifies the minimum number of sites needed for robust estimation of yield potential at local, regional, and national scales4,5,23,33.
The GYGA framework delimits climate zones (CZ) based on spatial variation in three key variables influencing crop growth and yield: growing degree-days, temperature seasonality, and aridity index5. The framework evaluates all CZs that account for >5% of total national harvested area for each crop (either irrigated or rainfed water regime). Within each CZ, buffer zones of 100-km radius (called “sites” in main text) were created around existing weather stations where measured weather data are retrieved, with each buffer “clipped” by CZs borders. For each crop-water regime, buffers were selected sequentially starting from the buffer with largest crop harvested area, including only buffers that account for >1% of national crop harvested area and minimizing overlap (<20%) among adjacent buffers, until approximately half of the national harvested area is covered for the target crop. Crop area distribution maps of maize and rice around 2005 (average for 2004-2006), disaggregated by water regime, were retrieved from the International Food Policy Research Institute (IFPRI—MAPSPAM database)34. MAPSPAM provides 10 x 10 km grid-cell resolution maps of harvested area for each of the major food crops. In a few cases (14%) there were no weather stations in areas where new cropland was established. Additional buffers were created in selected producing regions without MAPSPAM data or where there were no weather stations. In the last case, we used secondary gridded weather data from the NASA-POWER database35. A total of 50, 55, and 16 buffers were created for irrigated rice in China, rainfed or irrigated rice in Indonesia, and rainfed maize in Nigeria, respectively. Figure 2 only shows buffers with significant change in net area (greater than 15,000 or 10,000 hectares for rice and maize, respectively) in the 2000-2010 period.
Within each buffer, dominant soil types and crop management data were taken from the GYGA database to portray the dominant cropping system(s) used for simulation of annual yield potential. In summary, crop management, soil, and climate factors governing yield potential, as well as subnational data on current farm yields reported by government agencies were populated at the buffer level using observed data to the extent possible. Upscaled estimates of current yields and yield potential at CZ scales were based on aggregation of crop area-weighted values of all buffer zones within each CZ. A detailed description of the GYGA spatial upscaling methodology can be found elsewhere3,4,26.
Yield and substitution cost assessment
Three countries undergoing rapid urbanization during the last few decades were selected as case studies36. Crop area distribution in 2000 (average for 1999-2001) and 2010 (2009-2011)17 from MAPSAM was used to estimate net change in crop area for that 10-year period in each buffer and CZ, which in turn was used to identify areas of rapid crop expansion or contraction for maize (Nigeria) and rice (China, Indonesia). In the case of Nigeria, maize is grown under rainfed conditions, which means crop growth depends on stored soil water at sowing and in-season rainfall to meet its water requirements. In the case of rice, nearly all rice production in China occurs on irrigated land, while both irrigated and lowland rainfed rice are grown in Indonesia.
Locally calibrated crop models were employed to estimate yield potential of rice or maize in each buffer within each country14,24,25. We used Hybrid-Maize37 for maize simulations in Nigeria and Oryza V338 for rice simulations in China and Indonesia. Ten or 15 years of weather data were employed for yield potential and water-limited yield potential, respectively, as per GYGA methodology26. Soil and crop management data, including cropping intensity within each buffer were collected with the assistance of local agronomists. Current yields were obtained from official statistics at the lowest administrative level at which they are available within each buffer, for the most recent five years. Using a longer time period would bias estimation of current yields due to influence of a technology trend39. Details on the methodology followed to estimate yield potential in each country and data sources can be found in Supplementary Table S3 and elsewhere13,21,24.
Current yields and yield potential, as well as crop intensity and yield stability, in buffers experiencing large cropland substitution of rice or maize were compared with those at buffers where cropland is currently expanding (Figure 2). We estimated yields on an annual basis to account for the higher crop intensity in those regions where two or even three crops were produced each year on the same piece of land (rice in Indonesia and southern China). Then, for each country, we calculated the average annual yield (either current or potential) in CZs with expanded or contracted crop-specific area, weighted by the crop area net balance within each CZ (2000-2010). National average substitution costs were computed as the ratio between the weighted average yield in CZs with contracted area versus the weighted average yield in CZs with expanded crop area in the study period. For comparison, substitution costs were estimated separately based on either current yields (Supplementary Table 2) or potential yields as reported in Table 1. In this study, yields are reported at 15.5 and 14% seed moisture for maize and rice, respectively, which correspond to the commercial yield reporting standards for these crops.
Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.