Biogeographic limits of C4 engineered rice

doi:10.21203/rs.3.rs-3333949/v1

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Biogeographic limits of C₄ engineered rice

https://doi.org/10.21203/rs.3.rs-3333949/v1

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Genetic engineers are attempting to meet increased global food demand by rewiring the pathway crops take to convert sunlight and carbon dioxide (CO₂) into yield. Most crops use the C₃ pathway. C₄ photosynthesis uses a biological pump that concentrates CO₂, which makes it less resource demanding than C₃ photosynthesis. C₄ photosynthesis however uses more energy than C₃ photosynthesis, so the advantage of C₄ crops diminishes as atmospheric CO₂ concentrations increase and temperatures decrease. By estimating the trade-off between traditional C₃ and engineered C₄ rice, we show C₄ rice benefits less productive countries in the tropics rather than the largest producers (China, India). These geographic variations in productivity gains diminish under higher CO₂ concentrations and coincide with the likely commercial release of C₄ rice. Therefore, C₄ rice will have little advantage in terms of productivity over C₃ rice without global climate change mitigation. The findings agree with previous studies that projected declines in C₄ grasses as CO₂ concentrations increase despite the compensating effects of global warming. Researchers are exploring other yield improvement technologies—the benefits of which are also tied to climatic niches that change over time. Determining the limits of these technologies requires the biogeographic approach adopted in our study.

Biological sciences/Ecology/Agroecology

Biological sciences/Plant sciences/Photosynthesis/C4 photosynthesis

agriculture

food security

climate change

crop yield

photosynthesis

carbon

The Green Revolution was a tremendous achievement in agriculture in the second half of the 20^th century because it led to new selective plant-breeding programs and farming practices to meet global food demand from a rapidly growing human population¹. Early successes in breeding gave rise to semi-dwarf cereals with better fertilizer uptake, light-harvesting for photosynthesis, and resistance to lodging². The increased application of synthetic fertilizers, pesticides, and water delivery by large irrigation systems further enhanced crop growth and development. Engineered crops with higher photosynthetic efficiency are needed in the 21^st century to overcome the challenge of diminishing returns from the Green Revolution, rising atmospheric CO₂ concentrations and frequent weather extremes fueled by climate change, and the continued rise in global food demand³.

Three efficiencies control crop performance: (i) the marketable or consumable fraction of crop biomass, (ii) the fraction of light intercepted by the canopy, and (iii) the fraction of biomass accumulated versus the amount of light absorbed by the canopy⁴. The first two efficiencies (harvest index and light interception) will soon reach their biological limit for most cereals. Genetic engineers are shifting their attention to improving the third (photosynthetic) efficiency, which is well below its biological limit. Traditional breeding was a defining characteristic of the Green Revolution. It expressed desirable genetic traits in crops through field trials and artificial selection⁵. Genetic engineering, on the other hand, involves the direct manipulation of targeted crop genes in the laboratory. Improved photosynthetic efficiency is controlled by several genes, so optimizing a few targeted genes can diminish other beneficial genetic traits, which may limit the potential of this intervention to meet global food demands in the future⁶.

Genetic engineers are attempting to change the pathway crops take to metabolize atmospheric CO₂ during photosynthesis instead of optimizing the photosynthetic efficiency of one pathway⁷. Most crops use the C₃ pathway for photosynthesis. Crops that thrive in warm climates, such as maize, sorghum, sugarcane, and millet use the C₄ pathway⁸. The key difference between C₃ and C₄ photosynthesis concerns the delivery of CO₂ to the enzyme responsible for fixing CO₂ and making glucose that fuels crop growth and development: Ribulose-1,5-bisphosphate carboxylase-oxygenase (RubisCO). RubisCO has an affinity to both CO₂ and O₂. During the process known as photorespiration, atmospheric O₂ is fixed instead of CO₂ and less glucose is produced⁸. C₄ photosynthesis minimizes photorespiration using a biological pump that concentrates CO₂ at the active sites of RubisCO⁹. The C₄ pathway has a comparative advantage over the C₃ pathway in hot environments because RubisCO has a greater affinity to O₂ as temperatures rise¹⁰. C₄ photosynthesis uses more energy than C₃ photosynthesis to fuel the pump, so its benefits diminish as CO₂ concentrations increase and temperatures decrease¹¹.

The trade-off between C₃ and C₄ photosynthesis under increased CO₂ concentrations and temperatures is documented in experiments with grasses but few studies analyzed these dynamics over ecologically meaningful spatial scales and time frames¹². Several international research initiatives, such as the Bill and Melinda Gates Foundation funded C₄ rice project (https://c4rice.com), Realizing Increased Photosynthetic Efficiency (https://ripe.illinois.edu), and the European Commission funded "3to4: Converting C₃ to C₄ photosynthesis for sustainable agriculture" project, seek to introduce the C₄ pathway into rice and wheat. C₄ engineered crops require fundamental changes in plant anatomy and the Calvin cycle, which is the biochemical process that converts CO₂ to glucose¹³. They also require inserting and expressing the genes of C₄ photosynthesis. Such challenges are likely to delay the commercial release of C₄ engineered crops for 10 to 30 years. It is therefore unlikely that C₄ engineered crops will contribute to the Sustainable Development Goal to achieve “Zero Hunger” (SDG-2) by 2030¹⁴. The question is, can C₄ engineered crops fulfill the promise of a second Green Revolution and significantly increase global crop yields beyond 2030?

Ecological biogeography is the scientific field that examines how environmental factors, such as such as CO₂ fertilization and global warming, define the spatial distribution of plant and animal species across the globe¹⁵. We used the ecological approach to understand the trade-off of traditional C₃ and C₄ engineered rice production over space and time. Rice is the most important global food crop as it is the staple grain for more than half the world’s population⁹. We parameterized the Production Efficiency Model Optimized for Crops (PEMOC), which represents a broad class of light-use efficiency models first defined by Monteith¹⁶ and is appropriate for macroscale studies¹⁷. We used PEMOC to estimate the difference in expected crop yield for C₃ and C₄ rice from 1982 to 2013 across southern and eastern Asia—a region accounting for more than 90% of global rice production¹⁸. We compared the 1982‒2013 baseline to C₃ and C₄ rice yields projected to 2050 and 2100 under the four main Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathway (RCP) trajectories 2.6, 4.5, 6.0, and 8.5¹⁹.

Productivity gains by C₄engineered rice occurred mostly in the lower performing countries with less rice area. These gains diminished under higher projected CO₂ concentrations beyond 2030. The impact of global heating on photorespiration did not appear to outweigh future gains in traditional C₃ rice production under intermediate and pessimistic RCPs.

The crossover between C₃ and C₄ rice production during the 1982 to 2013 baseline was spatially clustered (Figure 1). The major rice-producing regions of northeast, coastal southeast, and west China, the largest producer, showed greater yields from C₃ than C₄ rice. Production in the Yangtze River basin in the middle of the country was more heterogenous. The second largest rice producer (India) had a west-east gradient. The Basmati rice growing states in the north and west of the country (Punjab, Haryana, Uttar Pradesh) benefited more from C₃ rice, while the major rice-producing regions of West Bengal and surrounding states in the east saw boosts in yield from C₄ rice. Greater C₄ rice yields in the east extended into Bangladesh, which is the third largest rice producing country. Major hotspots for C₄ rice production were estimated further south in the central plains of Thailand, Mekong River delta of Vietnam, and Java Island of Indonesia. In RCP 2.6, by 2050, the differences deepened for C₃ benefiting regions in the north, while many areas that showed modest increases in production from C₄ rice over the baseline in the south, shifted to modest increases in yield from C₃ rice. More pixels shifted from C₄ to C₃ rice as the scenarios became more pessimistic (RCP 4.5, 6.0, 8.5).

The clustering produced clear latitudinal crossovers. C₄ yields on average were greater than C₃ yields below 31°N latitude over the 1982‒2013 baseline (Figure 2), which accounts for approximately 71% of rice producing areas for southern and eastern Asia. The threshold dropped to 23°N latitude and represented only 33% of the rice area under RCP 2.6 by 2050. The boundary continued to move southward with each successive scenario. The latitudes for RCP 4.5, 6.0, and 8.5 were below 17°N, 16°N, and 15°N, which represented around 17%, 14%, and 12% of the current rice producing area, respectively. By 2100, C₄ rice yields were larger than C₃ rice yields only for RCP 2.6 (Figure A1). In this scenario, the cutoff rebounded to the 31°N latitude baseline. For RCP 4.5 and 6.0, C₃ yield was modestly higher at all latitudes (yield gain range=-1.76 to -0.19 and -2.50 to -0.34 t/ha). For RCP 8.5, C₃ yields were substantially higher at all latitudes, particularly below 15°N latitude (yield gain range=-5.10 to -0.81 t/ha).

Much of rice production in China is north of 31°N latitude, so C₄ rice tended to underperform for the baseline and all 2050 RCPs (Figure 3). Yield gains ranged from -0.57 (RCP 4.5) to -0.75 (RCP 6.0) t/ha. India is south of 31°N latitude, so it saw a benefit from C₄ rice for the baseline period (+0.34 t/ha) and RCP 2.6 (+0.02 t/ha). However, the yield gains were negative under the other scenarios (-0.24, -0.23, -0.29 t/ha for RCP 4.5, 6.0, 8.5). More tropical countries (Bangladesh, Indonesia, Vietnam, Thailand, Myanmar, Philippines) experienced boosts in yield from C₄ rice for the baseline period and under RCP 2.6. Under RCP 4.5, 6.0, and 8.5, however, C₃ rice yields were greater or the gains for C₄ rice were negligible. By 2100, sizable gains in yield from C₄ rice were observed in these countries only for RCP 2.6 (Figure A2). The underperformance of C₄ rice production for RCP 4.5, 6.0, and 8.5 for these countries was striking compared to 2050.

Our results show it is likely that C₄rice will have little advantage in terms of production over C₃ rice in the future unless global climate mitigation measures are taken to significantly lower CO₂ concentrations. Most C₄ plants evolved only 30 million years ago during the Oligocene epoch when atmospheric CO₂ concentrations were at their lowest level in the past 300 million years²⁰. Dramatic increases in oxygenase activity of RubisCO and photorespiration in the low CO₂ environment likely drove the selection for C₄ photosynthesis. Warming and to a lesser extent other factors that contribute to higher photorespiration, such as increased aridity, helped stagger C₄ selection across diverse ecosystems of the globe²¹. Fossil fuel combustion, agricultural expansion into forested areas, and other human activities are pushing atmospheric CO₂ concentrations to pre-Oligocene levels when conditions promoted low photorespiration. Doubling of atmospheric CO₂ in the next 100 years, which corresponds to RCP 6.0, could reduce the rate of photorespiration by half²². If the rise in CO₂ concentrations continue unabated as projected for RCP 8.5, photorespiratory activity may slow even more.

Our study considered the impact of average air temperature and CO₂ concentrations via photosynthetic efficiency on the performance of C₃ and C₄ rice yield. This is consistent with earlier biogeographic studies that employed the "quantum yield hypothesis” ^23–25. The hypothesis presumes photosynthetic efficiency is the principal determinant of C₄ success. Ehleringer²⁸ analyzed the impact of temperature with a simple light-use efficiency model. They observed a clear crossover around 45°N latitude in the Great Plains of North America for the month of peak productivity. They attributed the increase in C₄ productivity at lower latitudes to warmer temperature effects on photorespiration inhibition. The crossover observed in our study was 31°N latitude, which is 14° further south. Still et al.⁵⁴ used a land surface model to estimate GPP for C₃ and C₄ vegetation across the globe. They estimated a crossover around 15°N, which is even further south of our study. Like Still et al.⁵⁴, our model covered a much wider geographic area and therefore diversity of environments than the Ehleringer²⁸ study. It simulated production over the entire growing season instead of the month of peak productivity, which Ehleringer²⁸contended could push the crossover to a lower latitude. Our model considered other constraints on production independent of photosynthetic efficiency (f_PAR, f_A, f_T, and f_M) that were not included in Ehleringer²⁸.

Collatz et al.²⁹supports our key finding on the biogeographic limits of C₄ engineered rice in the future. The study developed a simple crossover relationship between temperature and CO₂ concentration from model experiments and analysis of plant distributions. They used the relationship to map past and future distributions of C₃ and C₄ grasses globally. They predicted the increase in CO₂ following the industrial revolution led to a 17% decline in C₄ grass area because of increased CO₂ concentrations. They projected a steady decline in C₄grass area with increased CO₂ concentrations in the future irrespective of the compensating effects of global warming. The evidence suggested C₄ vegetation may be completely overtaken by C₃ vegetation if the increase in CO₂concentrations continues unabated.

Our analytical approach has a few limitations that should be addressed in future studies. First, we ignored the compensating effects of all factors impacting production integrated together. We conducted a sensitivity analysis of PEMOC on a per pixel basis using Sobol’s method and found the model was most sensitive to incoming shortwave radiation over much of the study area. We did not analyze differences in incoming radiation because the quantum yield hypothesis assumes light-limited carbon uptake²⁵. Light-saturated conditions do occur and can pose a further restriction on carboxylase reactions that impede C₃ photosynthesis, but the effect is small. We assumed f_PAR, f_A, f_T, and f_M were parameterized in the same way for C₃and C₄ rice, but this may not always hold true. There is a cold-temperature limitation on C₄ photosynthesis, for example, that is not accounted for by f_T. Light-use efficiency models compensate for this with f_PAR, which is downregulated for C₄ vegetation under cold conditions. We did not account for this in our study, because we could not reasonably project f_PAR into the future. Without this offset, C₄ rice may be more productive at higher latitudes because of global warming.

Second, our approach did not consider other advantages of C₄ photosynthesis. Water-use efficiency in C₄ photosynthesis is higher because CO₂ is used more effectively and less water is lost via transpiration. C₄ crops therefore tend to perform better under drought conditions than C₃ crops. C₄ photosynthesis has higher nitrogen (fertilizer) use efficiency because less RubisCO is required for photosynthesis. RubisCO is the most abundant protein in plants and nitrogen occurs primarily as proteins in plants. The C₄ pathway may benefit rainfed rice greatly, since it is more resource intensive than irrigated rice, but we did not consider watering regimes in our study.

Third, we made certain model assumptions to accommodate the scale of the study and availability of data. The harvest index and constraints on GPP_MAX and respiration were constant, but they do vary to some degree for different rice varieties and environmental conditions. Production was only estimated for the primary growing season, but several agroecosystems in Asia include one or more rice growing seasons. The spatial resolution of the input data was ≥8×8km², which may be too coarse to represent field-level rotation patterns and multiple rice seasons.

Achieving C₄ photosynthesis in rice is an important goal to increase efficiency in yield gains while reducing water and fertilizer requirements. It is one of a basket of required technological advancements to continue to increase crop productivity in increasingly challenging environments, such as breeding to increase heat and salinity tolerance, and genetic engineering manipulations that increase yield whilst also improving water and nutrient use efficiency²⁶. Geographic targeting of such technologies is essential for generating realistic assessments of the spatial patterns of their impact and benefits. This targeting must also account for the likely timing of their widespread availability and adoption as a changing climate leads to changing benefits. Such modelling provides location and time specific guidance on best-best technology packages which can help prevent a repeat of the severe spatial disparities in benefits from the Green Revolution.

Production Efficiency Model Optimized for Crops (PEMOC). PEMOC calculates crop yield in Mt ha^− 1 on a pixel basis as the product of the harvest index and net carbon accumulated in crops over the growing season¹⁷. The harvest index is constrained by the conversion factor from carbon to biomass, proportion of aboveground biomass, and moisture content of the grain. These were set to 0.55, 1/0.45, 0.9, and 0.14 respectively for both C₃ and C₄ rice following Peng et al.²⁷ and Wang et al.²⁸. Biomass is the carbon gained from photosynthesis or gross primary productivity (GPP) minus the carbon lost during respiration in gC m^− 2 d^− 1. It was assumed GPP and respiration were conservative fractions (63% and 50%) of maximum GPP and GPP according to the original formulation, which was based on studies with in situ data^29,30. Maximum GPP (GPP_MAX) is the main engine of PEMOC (Table 1). It is a function of the maximum photosynthetic efficiency (ε_MAX), fraction of photosynthetically active radiation (f_PAR), three stressors (leaf age-f_A, temperature-f_T, moisture-f_M), and photosynthetically active radiation (PAR).

ε_MAX defines the photosynthetic pathway. We assume, in line with previous studies on the biogeography of C₃ and C₄ vegetation, that quantum yield and photosynthetic efficiency distinguish C₃ and C₄ performance the most^23–25. Therefore, other PEMOC constraints in this study were the same for C₃ and C₄ rice. The C₃ pathway is defined in terms of intercellular CO₂ concentration as a function of atmospheric CO₂ concentration (C_A) in µmol mol^− 1 and the CO₂ compensation concentration (Γ), which regulates the oxygenase (O₂) to carboxylase (CO₂) reactions for RubisCO³¹. Γ in the C₃ pathway is temperature dependent. As temperatures increase, oxygenase reactions outpace carboxylase reactions and vice versa. The C₄ pathway is essentially temperature independent and Γ is constant because oxygenase reactions are near zero³².

f_PAR is derived from the normalized difference vegetation index (NDVI)³³. NDVI is widely used in Earth Observation to monitor vegetation growth and development. NDVI, and by extension f_PAR, increase over the growing season as red absorption and near infrared (NIR) reflectance increase with greater chlorophyll content and leaf area of the vegetation. Leaves brown and lose their photosynthetic capacity following mid-season (peak) productivity. f_PAR is therefore downregulated by f_A after vegetation has achieved peak productivity (f_PAR,max)³⁴.

f_T accounts for excess heat and cold-limitations. It is expressed with a bell-shaped curve³⁵. Vegetation becomes more productive as air temperatures (T_A) increase until an optimum (T_OPT) is reached. Vegetation becomes less productive as temperatures exceed T_OPT.

f_M is calculated from vapor pressure deficit (VPD)³⁶, which essentially captures the difference between intracellular and atmospheric moisture content. As atmospheric moisture demand increases, less moisture is available to the vegetation from the soil³⁷. The relationship is expressed as a logarithmic decay function.

A unique feature of PEMOC is the coefficients for f_PAR, f_A, f_T, and f_M (a₀, a₁, a₂, a₃, and a₄) are calibrated with eddy covariance (EC) flux tower GPP and a Levenberg-Marquardt non-linear fitting algorithm to optimize model performance³⁸.

Table 1

GPP components of PEMOC
Parameter	Formula	Reference
GPP_MAX	\({{\epsilon }}_{\text{M}\text{A}\text{X}}{\text{f}}_{\text{P}\text{A}\text{R}}{\text{f}}_{\text{A}}{\text{f}}_{\text{M}}{\text{f}}_{\text{T}}\text{P}\text{A}\text{R}\)	Marshall et al.¹⁷
ε_MAX	\({C}_{3}:0.08\left({C}_{I}-\varGamma \right)/\left({C}_{I}+2\varGamma \right)\) \({C}_{4}:0.06\)	Collatz et al.^31,32
f_PAR	\({\text{a}}_{0}\text{N}\text{D}\text{V}\text{I}+{\text{a}}_{1}\)	Myneni and Williams ³³
f_A	\({\text{f}}_{\text{P}\text{A}\text{R}}/{\text{f}}_{\text{P}\text{A}\text{R},\text{m}\text{a}\text{x}}\)	Fisher et al.³⁴
f_T	\(1.1814/\left(\left(1+{\text{e}}^{{\text{a}}_{2}\left({\text{T}}_{\text{O}\text{P}\text{T}}-10-{\text{T}}_{\text{A}}\right)}\right)\left(1+{\text{e}}^{{\text{a}}_{3}\left({\text{T}}_{\text{A}}-10-{\text{T}}_{\text{O}\text{P}\text{T}}\right)}\right)\right)\)	Potter et al.³⁵
f_M	\(1-{\text{a}}_{4}\text{l}\text{n}\left(\text{V}\text{P}\text{D}\right)\)	Oren et al.³⁹

Eddy covariance (EC) flux tower data and PEMOC optimization. EC flux towers measure the exchange of carbon, water, and energy between the land surface and boundary layer atmosphere⁴⁰. We used three eddy covariance (EC) flux towers to calibrate the PEMOC model parameters (Table 2). We performed the calibration independent of the macroscale simulation because of data availability. The flux tower data corresponded to fields of irrigated lowland rice (Oryza sativa) varieties in Dongsong-eup, South Korea (CRK); Tsukuba, Japan (MSE); and Los Baños, Laguna, Philippines (RIF). CRK, MSE, and RIF are classified as warm continental, warm humid, and tropical climates, respectively. CRK and RIF data were acquired from AsiaFlux (https://www.asiaflux.net), while MSE data were downloaded from FLUXNET (https://fluxnet.org). The data are available half-hourly, hourly, or daily. Daily data were aggregated to a monthly timestep to align with PEMOC model assumptions on light-use efficiency and accommodate the temporal resolution of the gridded geospatial data. The three stations produced 84 months of calibration data over a seven-year period (2012 to 2018). The data consisted of carbon flux (µmol CO₂ m^− 2 s^− 1), incoming solar radiation (W m^− 2), T_A (°C), and VPD (kPa). GPP (µmol CO₂ m^− 2 s^− 1) was derived from eddy flux net CO₂ flux measurements using the daytime partitioning method. Data gaps in GPP were filled with the Marginal Distribution Sampling method. PAR (µmol m^− 2 d^− 1) was calculated from incoming solar radiation with the sw.to.par function in the R statistical computing software environment⁴¹. The sites do not measure red and NIR surface reflectance, so NDVI was instead computed from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra Vegetation Indices 16-Day Global 250m product (MOD13Q1)⁴². NDVI was extracted from the imagery for the 250m grid cell located at the center of each EC flux tower footprint with the National Aeronautics and Space Administration (NASA) Application for Extracting and Exploring Analysis Ready Samples sub-setting tool (https://appeears.earthdatacloud.nasa.gov). The footprint is the upwind contributing area to the EC flux tower. It varies throughout the year because of changing windspeed and direction. The footprint of each tower was defined in this study based on the prevailing winds during the primary growing season provided in the EC tower data. The maximum NDVI was calculated at a monthly timestep to correspond to the weather data.

Table 2

EC flux tower site information. The elevation, mean annual precipitation, mean annual temperature are denoted Elev, PPT, and MAT.
ID	Site Name	Latitude	Longitude	Period	Elev (masl)	PPT (mm)	MAT (°C)
CRK	Cheorwon Rice paddy	38°12'N	127°15'E	2015‒2018	181	1394	10.2
MSE	Mase paddy flux site	36°3'N	140°1'E	2012	13	1200	13.7
RIF	Philippines Rice Institute flooded	14°8'N	121°15'E	2013‒2014	21	2075	27.5

We performed the optimization of a₀, a₁, a₂, a₃, and a₄ with the Levenberg-Marquardt non-linear fitting algorithm in 13 iterations, which was well below the default limit (1000). The uppper and lower limits of the coefficients were defined according to Marshall et al.¹⁷ to ensure realistic optima. The optimization led to moderate-high correlations with the monthly EC flux tower GPP (R² = 0.71, RMSE = 2.00 gC m^− 2). The final coefficients were a₀ = 1.45, a₁ = 0.18, a₂ = 0.23, a₃ = 1.00, and a₄ = 0.23. The correlation and error were lower compared to the optimization of PEMOC in the original study with 815 daily GPP measurements collected by an EC flux tower in a lowland irrigated rice field in the United States (US-TWT: R² = 0.77, RMSE = 7.38 gC m^− 2)¹⁷. The coefficients tended to be lower in their study as well: a₀ = 1.00, a₁ = 0.20, a₂ = 0.17, a₃ = 1.00, and a₄ = 0.16. The differences can be attributed to the temporal scale of calibration (daily versus monthly) as well as the diversity of stations used in our study.

Earth observation and other gridded geospatial data. We simulated yield on an annual basis from 1982 to 2013 for each pixel (8×8km² spatial resolution) for traditional C₃ and C₄ engineered rice with the following gridded geospatial datasets: Asia rice map⁴³, NASA Global Inventory Monitoring and Modelling System (GIMMS) NDVI⁴⁴, agro-meteorological indicators from the European Environment Agency-5 (AgERA5)⁴⁵, and spatially resolved Coupled Model Intercomparison Project-6 (CMIP6) atmospheric CO₂ concentrations⁴⁶. The yield was based on monthly estimates of PEMOC GPP over the primary growing season. The start and end of season were estimated on a pixel basis with the NASA GIMMS NDVI and TIMESAT time series analysis software⁴⁷. The rice crop mask map was generated at 500m spatial resolution for southern and eastern Asia by the International Rice Research Institute with 2001 to 2012 MODIS time series data. The GIMMS dataset is available globally every 15 days at ~ 8×8km² spatial resolution from 1982 to 2015. NDVI is calculated from red and near infrared reflectance detected by the Advanced Very High-Resolution Radiometer. AgERA5 consists of hourly weather data for crop modeling (incoming solar radiation, T_A, VPD) at ~ 10×10km² spatial resolution from 1979 to present. The data is derived from European Centre for Medium-Range Weather Forecasts climate reanalysis. The CMIP6 product consists of monthly atmospheric CO₂ concentrations from 1850 to 2013 at ~ 100×100km² spatial resolution. The data is spatially disaggregated with Carbon Dioxide Information Analysis Center fossil field CO₂ emissions⁴⁸. We averaged the annual estimates of C₃ and C₄ rice yield over the 32-year period for comparison purposes with the RCP projections.

We projected traditional C₃ and C₄ engineered rice yield to 2050 and 2100 for RCP 2.6, 4.5, 6.0, and 8.5 with the agro-meteorological indicators from ERA-interim reanalysis⁴⁹ and the spatially resolved CMIP6 atmospheric CO₂ concentrations. As with the baseline, yield estimates were derived from monthly estimates of primary growing season PEMOC GPP. NASA GIMMS is not projected, so we instead used the 1982 to 2015 NDVI climatology only to scale f_PAR for the future period in line with other macroscale studies⁵⁰. The ERA-interim product consists of the CMIP5 ensemble mean of climate projections from several global circulation model experiments. It is available daily at ~ 50×50km² spatial resolution from 1951 to 2100. The baseline CMIP6 CO₂ concentrations are projected from 2015 to 2150 with an autoregressive integrated moving average function. The RCPs consist of different scenarios of radiative forcing, aerosols, greenhouse gasses, population growth, and land cover change¹⁹. RCP 2.6 is an optimistic scenario in which radiative forcing peaks at 3 W m^− 2 before 2100 and begins to decline. Global CO₂ concentrations achieve 420.89 ppm. RCP 8.5 is a pessimistic scenario in which radiative forcing exceeds 8.5 W m^− 2 by 2100 and continues to rise through the 22nd century. CO₂ concentrations reach 935.87 ppm in 2100. RCP 4.5 and 6.0 are intermediate scenarios. CO₂ concentrations stabilize at 4.5 and 6.0 W m^− 2 after 2100, respectively. CO₂ concentrations reach 477.67 and 669.72 ppm by 2100.

Yield comparison. We interpreted the spatial distribution of yield for traditional C₃ and C₄ engineered rice for the 1982‒2013 baseline and “future” scenarios (RCP 2.6, 4.5, 6.0, 8.5 for 2050 and 2100) with gridded maps, latitudinal plots, and country-wise statistics. The spatial maps consisted of simple differences (C₄ - C₃) masked for rice pixels. Positive values indicated better performance of C₄ rice, while negative values indicated better performance of C₃ rice. The pixel values were averaged by grid row to see if there was a clear latitudinal boundary for better C₄ performance. Finally, the pixels were averaged by country to see how yield differences impacted the top eight rice producing countries of the world at present (China, India, Bangladesh, Indonesia, Vietnam, Thailand, Myanmar, and Philippines).

Author Contributions

Michael Marshall (MM) and Deepak Poothakuzhiyil Haridasan (DPH) conceptualized the project. MM developed the PEMOC model, acquired and processed GIMMS and ERA5-Land, processed EC flux tower data, performed model optimization, wrote codes supporting the technical workflow in R, and contributed considerably to writing the original draft. DPH acquired and processed ERA-Interim, translated R scripts into Python, and wrote and applied the Sobol sensitivity analysis script in Python. Florencio Campomanes V (FC) acquired and processed the gridded CO₂ data, as well as enhanced Python scripts to address deficiencies in the preliminary analysis. MM, DPH, and FC were responsible for the formal analysis and visualization for the project. DPH and FC contributed to writing the original draft and curated the data over the duration of the project. Christiaan van der Tol and Anton Vrieling were responsible for review and editing of the original draft, as well as co-supervision. Kevin Tu and Andy Nelson contributed to writing the original draft. Lilin Zhang acquired and processed the EC flux tower data.

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Biogeographic limits of C₄ engineered rice

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