Global cropland net primary production (NPP) has tripled over the
last 50 years, contributing 17–45 % to the increase in global
atmospheric CO
Cropland net primary production (NPP) plays a crucial role in both food
security and atmospheric CO
Features of the agricultural Green Revolution across regions.
Globally, agricultural areas cover
Three methods are available for estimating vegetation NPP: statistical data, process-based models, and remote sensing. Statistical data and process-based models are the prevalent methods for estimating global NPP, but, except for a few recent studies, are generally limited to natural vegetation based on climate and edaphic variables, (Gray et al., 2014; Zeng et al., 2014). Therefore, global- and regional-scale estimates of cropland NPP must rely on census and survey data. However, these data report agricultural production, not NPP, and thus need crop-specific factors (dry matter fraction, harvest index (HI), root-to-shoot ratio, etc.) to calculate the NPP (Gray et al., 2014; Huang et al., 2007; Monfreda et al., 2008; Prince et al., 2001), which neglected the temporal evolution for crop-specific factors such as HI and root-to-shoot ratio (Lorenz et al., 2010; Sinclair, 1998). Remote sensing using satellites is a powerful tool for estimating global terrestrial NPP (Cleveland et al., 2015; Field et al., 1995; Nemani et al., 2003; Parazoo et al., 2014; Zhao and Running, 2010), yet croplands are coincident with natural vegetation, making it difficult to differentiate the two using remote sensing (Defries et al., 2000; Monfreda et al., 2008).
The current state of the global carbon models is as follows: (1) some models, such as Lund–Potsdam–Jena (LPJ) or ORCHIDEE, do not have an agricultural module; (2) models with an agricultural module, such as LPJ managed Land (LPJmL), do not fully represent the features of the Green Revolution; (3) the Vegetation–Global Atmosphere–Soil (VEGAS) model, by Zeng et al. (2014), was the first attempt to model the agricultural Green Revolution. The importance of parameter calibration has been recognized and addressed by numerous modeling studies (Bondeau et al., 2007; Chen et al., 2011; Crowther et al., 2016; Luo et al., 2016; Ogle et al., 2010; Peng et al., 2013). In addition, regional calibrated parameters are critical for global-scale modeling (Le Quéré et al., 2016). However, because the management data needed for most terrestrial models are spatially and temporally scarce, a precise regional simulation and calibration seems impossible (Bondeau et al., 2007).
Here, we conducted a study concentrated on calibrations on both the regional and the country scales. Instead of using an extensive set of actual management data that are unavailable or incomplete, we modeled the first-order effects on crop NPP using parameterizations. Our objectives were to (1) describe the method for simulating the three Green Revolution features, (2) quantify the cropland NPP over the last 50 years on both the continental and country scales, and (3) improve the model's performance by key parameterization.
We simulated agriculture using a generic crop functional type that represents an average of three dominant crops: maize, wheat, and rice. These crops are similar to warm C3 grass, one of the natural plant functional types in VEGAS (Zeng et al., 2005a, 2014). A major difference is the narrower temperature growth function, to represent a warmer temperature requirement than natural vegetation. Cropland management is modeled as an enhanced photosynthetic rate by the cultivar selection, irrigation, and application of fertilizers and pesticides. We modeled the first-order effects on the carbon cycle using regional-scale parameterizations with the following rules.
The selection of high-yield dwarf crop varieties has been a key feature of
the agricultural Green Revolution since the 1960s, generally accompanied by
an increase in the HI (the ratio of grain to aboveground biomass)
(Sinclair, 1998). The HI varies for different crops, with a
lower value for wheat (0.37–0.43) (Huang et al., 2007; Prince et al.,
2001; Soltani et al., 2004) and higher values for rice (0.42–0.47)
(Prasad et al., 2006; Witt et al., 1999) and maize
(0.44–0.53) (Huang et al., 2007; Prince et al., 2001). We used a
value of 0.45 for the year 2000, a typical value of the three major crops:
maize, rice, and wheat (Haberl et al., 2007; Sinclair, 1998). The
temporal change in HI is modeled as
Harvest index change over time as used in the model and a harvest index of 0.31 in 1961 and 0.49 in 2010, based on literature review.
Irrigation intensity (
To represent the effect of irrigation, the soil moisture function (
To represent the enhanced productivity from cultivar and fertilization, the
gross carbon assimilation rate is modified by a management intensity (MI) factor
that varies spatially and changes over time:
Default and calibrated regional management intensity parameter of
Management intensity (relative to year 2000) changes over time as used in the model. The analytical functions are hyperbolic tangent (see text). The parameter values correspond to a management intensity in 1961 that is 10 % smaller than in 2010.
Parameter The
After the two steps, total production was summed as all countries with
updated parameters.
Crop phenology was not decided beforehand but was determined by the climate condition. For example, when it is sufficiently warm in temperate and cool regions, crops begin to grow. This assumption captures most of the spring planting and simulates multiple cropping in low latitudes. However, one limitation of such a simple assumption is that it misses some other crop types such as winter wheat, which has an earlier growth and harvest.
When the leaf area index growth rate slows to a threshold value, a crop is assumed to be mature and is harvested. The automatic planting and harvest criteria allow multiple cropping in some warm regions and match areas with intense agriculture such as East Asia and Southeast Asia, but the criteria may overestimate regions with single cropping. Consequently, the simulated results tend to be the potential productivity due to the climate characteristics and our generic crop.
After harvest, grain and straw are assumed to be appropriated by farmers and
then incorporated into the soil metabolic carbon pool. The harvested crop is
redistributed according to population density, resulting in the horizontal
transport of carbon. As a consequence, cropland areas act as net carbon
sinks, and urban areas release large amounts of CO
Gridded monthly climate data sets (i.e., maximum and minimum temperature,
precipitation, and radiation) covering the period 1901–2013 with a spatial
resolution of 0.5
The land cover data set (crop and pasture versus natural vegetation) was derived
from the History Database of the Global Environment (HYDE) data set
(
Crop production and cropland area are aggregated from FAO statistics for the
major crops (FAOSTAT,
The VEGAS model used in TRENDY (Sitch et al., 2015; Zeng et al., 2005a) was
run from 1700 to 2010 and forced by climate, annual mean CO
The agricultural Green Revolution was mostly started in the 1960s to cope
with the food–population balance, particularly in developing countries
(Borlaug, 2002) (Table 1). Its features include the development of
high-yield varieties of cereal grains, the expansion of irrigation,
and applications of synthetic fertilizers and pesticides (Borlaug, 2007).
The intensity of such management varies widely and has not always occurred
synchronously in different parts of the world. Specifically, in the 1950s,
new wheat and maize varieties were developed by the International Maize and
Wheat Improvement Center (CIMMYT) in Mexico, and their agricultural
productivity increased with irrigated cultivation in the northwest
(Byerlee and Moya, 1993; Gollin, 2006; Pingali, 2012). Later in 1966, a
new dwarf high-yield rice cultivar, IR8, was bred by the International Rice
Research Institute (IRRI) in the Philippines, and it was spread and grown in
most of the rice-growing countries of Asia, Africa, and Latin America
(Fischer and Cordova, 1998; Khush, 2001; Peng et al., 1999). Also in the
1960s, India imported new wheat seed from CIMMYT to Punjab and later adopted
the IR8 rice variety from Philippines that could produce more grains
(Parayil, 1992). China began participating in the Green Revolution in the
1970s, with hybrid rice bred by Longping Yuan (Yuan, 1966), and the
fertilizer application rate increased dramatically from 43 kg ha
Worldwide, the FAO data showed that cropland production increased from 439 TgC in 1961 to 1519 TgC in 2010 (246 % increase) (Fig. 4), and the VEGAS model captured most of this trend in both the default and the calibrated results. East Asia and North America contributed the most to this trend (Fig. 5). For East Asia, crop production increased from 65 TgC in 1961 to 342 TgC (426 % increase) in 2010. For North America, it increased from 90 TgC in 1961 to 235 TgC (161 % increase) in 2010. Other regions followed the increasing trend except for the former USSR region. The lowest crop production existed in central-west Asia and Oceania, with less than 50 TgC over the study period.
Annual global crop production from 1961 to 2010. Default parameters were derived from a previous version that was used in Zeng et al. (2014) to capture the global trends, and calibrated parameters were set in this study (see text) to capture the regional trends.
Annual crop production from 1961 to 2010 on a continental scale.
The
Annual crop production from 1961 to 2010 on a country scale.
As described in Sect. 2.1.4, we calibrated the
Furthermore, the updated parameters in different regions did not substantially change the total production estimations (Fig. 4), indicating that a good agreement in global total production may be overestimated in some regions while underestimated in others, which does not reflect the true nature of the production distributions and variations.
At the country level, the FAO data showed that China, the USA, and India were the top three countries contributing to global crop production (Fig. 6). For China, crop production increased from 50 TgC in 1961 to 230 TgC in 2010 (360 % increase). For the USA, it increased from 76 TgC in 1961 to 204 TgC in 2010 (168 % increase). Other countries followed the same increasing trend with different rates. The lowest crop production in the top nine countries existed in Canada and Argentina, with less than 50 TgC over the study period.
As for the VEGAS simulations, the default parameters (Table 3) might overestimate results in some countries while underestimating others. The calibrated parameter could capture variations in most of the countries (Fig. 6). For Chinese crop production, a decreasing trend after 1999 was captured, but the magnitude was weaker (Fig. 6a) because the drop in cropland area was not represented in HYDE 3.0 for China. The calibrated parameter also performed well in other countries. For Brazil and Argentina, the dramatic increase after 2000 was not well captured due to the simple assumption that the strongest management occurred in 2000 and became weaker afterwards.
Default and calibrated national management intensity parameter of
Country-based comparison of simulated and observed cropland
productions (Tg) before
Based on the country-scale comparisons between the updated VEGAS simulations
and the FAO statistical data of the decadal means, the linear regression
slope was 1.00, with a higher
The two independent data sets produced similar spatial distributions of crop
NPP (Fig. 8). The highest crop NPP regions were the Great Plains of North
America and temperate western Europe and East Asia (> 1.0 Tg per
2500 km
Mean cropland NPP from 1997 to 2003. VEGAS modeled patterns (Tg C per 2500 km
Cereal and soybean NPP on a continental scale over the last 60 years derived from FAO yield data. Note that the scales are different.
The average cereal NPP increased from 1.0 to 1.5 Mg ha
In the estimation of crop NPP, one of the sources of uncertainty is crop
parameters, such as variations in HI. When accounting for this
variation of 0.45 (0.37–0.53, or 18 % of the mean), the uncertainty
resulted from the HI for the FAO production-derived NPP would be
1.3
Gray et al. (2014) used production statistics and a carbon accounting
model to show that increases in agricultural productivity explained
European cropland NPP increased 127 % over the last half century, as estimated by VEGAS (Fig. 5i), and the yield increased at a rate of 1.8 % per annum. Moreover, without the management intensity parameter updated, the crop yields for the 2000s would be 10.4 % lower. Similarly, a study showed that across all major crops cultivated in the EU, plant breeding has contributed approximately 74 % of total productivity growth since 2000, equivalent to a yield increase of 1.2 % per annum. European crop yields today would be more than 16 % lower without access to improved varieties (the British Society of Plant Breeders, BSPB). The 2003 drought and heat in Europe reduced the terrestrial gross primary productivity (GPP) by 30 % (Ciais et al., 2005), while it was decreased by 15 % for cropland NPP in this study (Fig. 5i). This decrease was smaller than the natural ecosystem response due largely to the counteractive effects of management inputs (irrigation, fertilization, etc.).
In the central USA, VEGAS modeled the cropland NPP as
> 6 MgC ha
In Asian croplands, the percentage of harvested area for rice, wheat, and
maize under modern varieties was lower than 10 % in the 1960s, and it
increased to over 80 % in the 2000s (Evenson, 2005). Moreover,
nitrogen (N) fertilizer increased from 23.9 kg ha
The African croplands currently nourish over 1.0 billion people. The need
for sustainable agriculture combined with stable grain yield production is
particularly urgent in Africa. However, the continent is now trading carbon
for food. Newly cleared land in the tropics releases nearly 3 tons of carbon
for every 1 ton of annual crop yield compared with a similar area cleared in
the temperate zone (West et al., 2010). This continent can
triple its crop yields, provided the depletion of soil nutrients is addressed
(Sánchez, 2010). Using chemical fertilizer as an example, the average
N application rate from 2002 to 2012 was only
In terms of the data gap in MI, very few data sets provide long-term time series data with high spatial resolution. HYDE is a land use data set that does not provide MI information (Goldewijk et al., 2011). Monfreda et al. (2008) developed a data set consisting of 175 crops consistent to the FAO statistical data for the period around year 2000. Moreover, Fritz et al. (2015) developed a cropland percentage map for the baseline year 2005. For the fertilizer data set, Potter et al. (2010) provided the global manure N and P application rate for a mean state around year 2000. Furthermore, Lu and Tian (2017) developed a global time series gridded data set for the synthetic N and phosphorous (P) fertilizer application rate in agricultural lands. For the irrigation data set, global monthly irrigated crop areas around the year 2000 were developed by Portmann et al. (2010). These data sets are mostly for a specific year or a period mean, and they are unsuitable for long-term simulations. Therefore, we still lack a comprehensive data set that reflects MI.
A more challenging task would be to calibrate regional parameters and explain spatial patterns better because models may significantly underestimate the high-latitude trend (Graven et al., 2013) and overestimate elsewhere even if the global total is simulated correctly (Zeng et al., 2014). More work should be directed to reduce uncertainties in regional model parameterizations (Le Quéré et al., 2015; Luo et al., 2016). This paper focuses on both the continental and country scales to calibrate key parameters to better constrain the future projections of global cropland NPP.
We used a process-based terrestrial model VEGAS to simulate global cropland
production from 1960 to 2010 and adapted the management intensity parameter
on both continental and country scales. The updated parameter could capture
the temporal dynamics of crop NPP much better than the default ones. The
results showed that cropland NPP tripled from 1.3
Several publicly available data sets were used in this study. The specific references and internet links to the data sources are given in the text. Model outputs are available upon request.
NZ conceived and designed the study; PFH and FZ performed the simulations and analyzed the results. NZ and PFH prepared the paper with contributions from all co-authors.
The authors declare that they have no conflict of interest.
This research was supported by the National Key R&D Program of China (no. 2017YFB0504000), the Thousand Talents Program Foundation of China (no. Y763012601), and the Postdoctoral Science Foundation of LASG Dean (grant no. 7-091162). Edited by: Govindasamy Bala Reviewed by: two anonymous referees