Mengru Wang1,2, Lin Ma1, Maryna Strokal2, Wenqi Ma3, Xuejun Liu4, Carolien Kroeze2. 1. Key Laboratory of Agricultural Water Resources, Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology , Chinese Academy of Sciences , 286 Huaizhong Road , Shijiazhuang 050021 , China. 2. Water Systems and Global Change Group , Wageningen University and Research , Droevendaalsesteeg 4 , Wageningen , 6708 PB , The Netherlands. 3. College of Resources and Environmental Sciences , Agricultural University of Hebei , Baoding , 071001 , China. 4. College of Resources and Environmental Sciences , China Agricultural University , Beijing 100193 , China.
Abstract
Food production in China results in large losses of nitrogen (N) and phosphorus (P) to the environment. Our objective is to identify hotspots for N and P losses to the environment from food production in China at the county scale. To do this, we used the NUFER (Nutrient flows in Food chains, Environment and Resources use) model. Between 1990 and 2012, the hotspot area expanded by a factor of 3 for N, and 24 for P. In 2012 most hotspots were found in the North China Plain. Hotspots covered less than 10% of the Chinese land area, but contributed by more than half to N and P losses to the environment. Direct discharge of animal manure to rivers was an important cause of N and P losses. Food production was found to be more intensive in hotspots than in other counties. Synthetic fertilizer use and animal numbers in hotspots were a factor of 4-5 higher than in other counties in 2012. Also the number of people working in food production and the incomes of farmers are higher in hotspots than in other counties. This study concludes with suggestions for region-specific pollution control technologies for food production in China.
Food production in China results in large losses of nitrogen (N) and phosphorus (P) to the environment. Our objective is to identify hotspots for N and P losses to the environment from food production in China at the county scale. To do this, we used the NUFER (Nutrient flows in Food chains, Environment and Resources use) model. Between 1990 and 2012, the hotspot area expanded by a factor of 3 for N, and 24 for P. In 2012 most hotspots were found in the North China Plain. Hotspots covered less than 10% of the Chinese land area, but contributed by more than half to N and P losses to the environment. Direct discharge of animal manure to rivers was an important cause of N and P losses. Food production was found to be more intensive in hotspots than in other counties. Synthetic fertilizer use and animal numbers in hotspots were a factor of 4-5 higher than in other counties in 2012. Also the number of people working in food production and the incomes of farmers are higher in hotspots than in other counties. This study concludes with suggestions for region-specific pollution control technologies for food production in China.
Nitrogen (N) and phosphorus (P) applications
in food production
greatly contribute to global food security as essential nutrients
for the growth of plants and animals. However, this can also lead
to N and P losses to the environment.[1−6] Increasing losses of N and P to surface waters and the atmosphere
have negative impacts on ecosystems and biodiversity. For example,
increased N and P losses from food production may cause eutrophication
and harmful algae blooms in many coastal areas around the world.[7−11]N and P losses from food production including crop and animal
production
in China have been increasing since the 1980s.[12−16] Ma et al.[17] estimated
that total P losses from crop production in 2005 were 300% higher
than that in 1980, while from animal production P losses were more
than 42 times as high as that in 1980 in China. This implies that
an increasing amount of nutrients was lost to air and waters.[4,9,18−21] Various studies discussed the
reasons why nutrient losses in China are high and increasing.[4,9,19,21−25] An important reason is poor nutrient management technologies in
food production: overuse of synthetic fertilizers and animal feeds,
low productivity, and poor management of animal manure. However, most
existing analyses are limited to the national or provincial levels,
and they often do not discuss the possible relations between nutrient
losses and socio-economic conditions that are usually the drivers
of intensive food production in China.[22,24−27] A more detailed analysis of N and P losses from food production
at, for instance, the county scale does not exist. Such an analysis
is highly relevant because it can provide more explicit quantitative
information on N and P flows in food production in China. The result
will contribute to identifying the “hotspots” where
N and P losses from food production are higher than other regions.
Analyzing the N and P flows in the hotspots and local agricultural
and socio-economic indicators may help to develop region-specific
nutrient management technologies and policies to reduce the potential
for nutrient pollution in China.Thus, our study aims to identify
hotspots for N and P losses to
the environment from food production in China at the county scale.
To do this, we used the NUFER(28) (Nutrient flows in Food chains, Environment, and Resources
use) model. We analyzed N and P losses from food production including
n class="Gene">crop and animal production for all Chinese counties in 1990, 2000,
and 2012. We also quantified the associated N and P use efficiencies
of food production in China to better understand the high losses in
the hotspots. We compared several agricultural and socio-economic
indicators for the hotspots with that for other counties in 2012.
On the basis of the results, we conclude with suggestions on technologies
and policies for nutrient pollution control in food production of
China.
Materials and Methods
NUFER Model
In
this study we used
the NUFER(17,28) model to quantify N
and P losses from food production for all counties in China for 1990,
2000, and 2012. Food production includes n class="Gene">crop anpan>d anpan>imal production
inpan> this study (Figure S1 inpan> Supportinpan>g Inpan>formation). Years 1990, 2000, anpan>d 2012 were selected to reflect the period
durinpan>g which the tranpan>sition of food production inpan> agriculture from
traditional small scale to inpan>tensive large scale took place inpan> Chinpan>a
(see section S1 inpan> the Supportinpan>g Inpan>formation for more inpan>formation about the tranpan>sition). The originpan>al NUFER model was developed by Ma et al.[28] to quanpan>tify N anpan>d P flows inpan> the food production, processinpan>g
anpan>d consumption chainpan> of Chinpan>a usinpan>g a mass balanpan>ce approach. This
model calculates nutrient flows at the national level for each year
from 1980 to 2010, anpan>d for the year 2030. Inpan> addition, regional nutrient
flows canpan> be calculated for 31 provinpan>ces for 2005 anpan>d 2013. Detailed
model description on the originpan>al NUFER model is
available inpan> section S2 inpan> the Supportinpan>g Inpan>formation. We developed anpan>d applied NUFER for all Chinpan>ese
counties usinpan>g county inpan>formation,[29] which
was not done before. We also improved NUFER by inpan>cludinpan>g
dry atmospheric N deposition on arable lanpan>d that was not inpan>cluded
inpan> the originpan>al model. We described inpan> section S2 inpan> the Supportinpan>g Inpan>formation how we inpan>cluded dry atmospheric
N deposition anpan>d applied NUFER to the county scale.
Identifying Hotspots
We quantified the N and P losses
to waters and the air from food production including crop and animal
production systems using NUFER. The losses to waters
are the N and P losses from leaching, surface runoff, and erosion
in crop production, and N and P losses from direct discharge of animal
manure in animal production. For the losses to the air, we calculated
the emissions of ammonia (NH3) and nitrous oxide (N2O) from crop and animal production systems. The details in
the calculation of N and P losses using NUFER are
available in section S2 in the Supporting Information. The calculated N and P losses in all counties were averaged by
the area of the counties and were grouped into four groups (Figures and 2). The intervals for the four groups were defined based on
quantiles (25%, 50%, 75%) of the averaged N and P losses of all counties
in 2012: group I (0–25%), group II (25–50%), group III
(50–75%), group IV (75–100%, hotspot). The top 25% were
considered hotspots. For 1990, 2000, and 2012 counties were considered
as hotspots if their N and P losses fall within the range of the top
25% for 2012. Thus, for all three years counties that have total N
losses exceeding 9625 kg N km–2 year–1 were qualified as hotspots (Figures and S2). For P, counties
with total losses of more than 905 kg P km–2 year–1 were qualified as hotspots (Figures and S3). We did
not identify hotspots based on water or air quality standards in China,
because these standards differ among regions (e.g., provinces) and
the use of the water body. For example, the water quality standards
for drinking water supply differ from standards for water that are
used for agricultural purposes. Therefore, we used the top 25% for
2012 as a basis for the identification of hotspots as shown in Figures and 2. We also calculated the associated N and P use efficiencies
of food production for a better understanding of N and P losses. N
and P use efficiencies were calculated as outputs of N and P via main
products divided by the total inputs of N and P to this system (Box S3).
Figure 1
Nitrogen (N) losses (kg N km–2 year–1) to the air and waters from leaching, runoff,
and erosion, direct
discharge of manure, ammonia (NH3), and nitrous oxide (N2O) emissions, and the total N losses (kg N km–2 year–1) from food production in 1990 and 2012.
The N losses were quantified using the NUFER model.
The intervals for the four groups in this figure were defined based
on quantiles (25%, 50%, 75%) of N losses of all counties in 2012:
group I (0–25%), group II (25–50%), group III (50–75%),
group IV (75–100%). Counties in group IV were qualified as
hotspots. The same information for 2000 is available in Figure S2. The names of the Agro-Ecological Zones
are available in Figure S8.
Figure 2
Phosphorus (P) losses (kg P km–2 year–1) to waters from leaching, runoff and erosion, direct
discharge of
manure, and the total P losses (kg P km–2 year–1) from food production in 1990 and 2012. The P losses
were quantified using the NUFER model. The intervals
for the four groups in this figure were defined based
on quantiles (25%, 50%, 75%) of P losses of all counties in 2012:
group I (0–25%), group II (25–50%), group III (50–75%),
group IV (75–100%). Counties in group IV were qualified as
hotspots. The same information for 2000 is available in Figure S3. The names of the Agro-Ecological Zones
are available in Figure S8.
Nitrogen (N) losses (kg N km–2 year–1) to the air anpan>d waters from leachinpan>g, runoff,
anpan>d erosion, direct
discharge of manpan>ure, pan> class="Chemical">ammonia (NH3), and nitrous oxide (N2O) emissions, and the total N losses (kg N km–2 year–1) from food production in 1990 and 2012.
The N losses were quantified using the NUFER model.
The intervals for the four groups in this figure were defined based
on quantiles (25%, 50%, 75%) of N losses of all counties in 2012:
group I (0–25%), group II (25–50%), group III (50–75%),
group IV (75–100%). Counties in group IV were qualified as
hotspots. The same information for 2000 is available in Figure S2. The names of the Agro-Ecological Zones
are available in Figure S8.n class="Chemical">Phosphorus (P) losses (kg P km–2 year–1) to waters from leachinpan>g, runoff anpan>d erosion, direct
discharge of
manpan>ure, anpan>d the total P losses (kg P km–2 year–1) from food production inpan> 1990 anpan>d 2012. The P losses
were quanpan>tified usinpan>g the NUFER model. The inpan>tervals
for the four groups inpan> this figure were definpan>ed based
on quanpan>tiles (25%, 50%, 75%) of P losses of all counties inpan> 2012:
group I (0–25%), group II (25–50%), group III (50–75%),
group IV (75–100%). Counties inpan> group IV were qualified as
hotspots. The same inpan>formation for 2000 is available inpan> Figure S3. The names of the Agro-Ecological Zones
are available inpan> Figure S8.
We included in our result (Figures , 2, S2, and S3) borders of the nine Agro-Ecological Zones (AEZs) in
China to illustrate the spatial distribution of the hotspots for N
and P losses. The AEZs are Northeast China, Inner Mongolia and Great
Wall Vicinity, North China Plain, Loess Plateau, Middle and Lower
Yangtze River, Southwest China, South China, Gansu and Xinjiang, and
Tibetan Plateau (see Figure S8 for the
location of the AEZs). These zones were defined by Sun and Shen[30] based on their similarities incrop production
(e.g., crop land, crop types) and animal production (e.g., animal
type, animal numbers) systems.
Comparing Agricultural
and Socio-economic Indicators in Hotspots
with Nonhotspots
We compared several agricultural and socio-economic
indicators in hotspots (group IV in Figures and 2, and in Figures S2 and S3) and nonhotspot counties (groups
I, II, III in Figures and 2, and in Figures S2 and S3) in 1990 and 2012. The agricultural indicators are
(i) N and P inputs to cropland from synthetic fertilizer, (ii) animal
numbers, (iii) share of sown area of vegetable and fruit to total
sown area, and (iv) N and P use efficiencies of food production including
crop and animal production. The socio-economic indicators are (i)
urban population, (ii) rural labor, (iii) total output value of agriculture
and forestry, and (iv) farmers’ incomes. We selected all socio-economic
indicators that are available in county statistics. The Tukey’s
Honest Significant Difference (Tukey’s HSD) method was used
to make pairwise comparisons of the agricultural and socio-economic
indicators among the groups I, II, III, and IV that was defined above.
The results of Tukey’s HSD comparison are shown in Figures and 4, and in Figures S4 and S5.
Figure 3
Boxplots for
nitrogen (N): synthetic fertilizer (ton km–2 year–1), animal number in livestock unit (lu km–2 year–1, see Supporting Information for converting animal numbers in livestock
unit), share of sown area of vegetable and fruit to the total sown
area (%), N use efficiency (NUE) of food production, urban population
(% of the total population), rural labor (capita km–2 year–1), total output value of agriculture and
forestry (billion yuan km–2 year–1), and farmers’ incomes (1000 yuan capita–1 year–1) among the four groups of total N losses
(see Figure ) in 2012
(A), and the pairwise comparisons from Tukey’s Honest Significant
Difference (Tukey’s HSD) among the four groups (B). In B panels
any 95% confidence intervals that do not contain 0 provide evidence
of a difference in the groups.
Figure 4
Boxplots of for phosphorus (P): synthetic fertilizer (ton km–2 year–1), animal number in livestock
unit (lu km–2 year–1, see Supporting Information for converting animal
numbers in livestock unit), share of sown area of vegetable and fruit
to the total sown area (%), P use efficiency (PUE) of food production,
urban population (% of the total population), rural labor (capita
km–2 year–1), total output value
of agriculture and forestry (billion yuan km–2 year–1), and farmers’ incomes (1000 yuan capita–1 year–1) among the four groups of
total P losses (see Figure ) in 2012 (A), and the pairwise comparisons from Tukey’s
Honest Significant Difference (Tukey’s HSD) among the four
groups (B). In B panels any 95% confidence intervals that do not contain
0 provide evidence of a difference in the groups.
Boxplots for
nitrogen (N): synthetic fertilizer (ton km–2 year–1), animal number in livestock unit (lu km–2 year–1, see Supporting Information for converting animal numbers in livestock
unit), share of sown area of vegetable and fruit to the total sown
area (%), N use efficiency (NUE) of food production, urban population
(% of the total population), rural labor (capita km–2 year–1), total output value of agriculture and
forestry (billion yuan km–2 year–1), and farmers’ incomes (1000 yuan capita–1 year–1) among the four groups of total N losses
(see Figure ) in 2012
(A), and the pairwise comparisons from Tukey’s Honest Significant
Difference (Tukey’s HSD) among the four groups (B). In B panels
any 95% confidence intervals that do not contain 0 provide evidence
of a difference in the groups.Boxplots of for phosphorus (P): synthetic fertilizer (ton km–2 year–1), animal number in livestock
unit (lu km–2 year–1, see Supporting Information for converting animal
numbers in livestock unit), share of sown area of vegetable and fruit
to the total sown area (%), P use efficiency (PUE) of food production,
urban population (% of the total population), rural labor (capita
km–2 year–1), total output value
of agriculture and forestry (billion yuan km–2 year–1), and farmers’ incomes (1000 yuan capita–1 year–1) among the four groups of
total P losses (see Figure ) in 2012 (A), and the pairwise comparisons from Tukey’s
Honest Significant Difference (Tukey’s HSD) among the four
groups (B). In B panels any 95% confidence intervals that do not contain
0 provide evidence of a difference in the groups.
Results
Hotspots for N and P Losses
The
N and P losses increased
fast between 1990 and 2012. As a result, the hotspot area in China
expanded during this period. The hotspot area for total N losses increased
by a factor of 3 (from 307 850 to 828 205 km2), and for total P by a factor of 24 (from 35 355 to 861 781
km2) between 1990 and 2012 (Table S6). The hotspots covered less than 5% of total land and contributed
to 28% of total N losses, and 10% of total P losses in food production
of China in 1990. In 2012, the hotspot area for total N and total
P losses expanded n class="Species">to 9% of the total lanpan>d inpan> Chinpan>a. Astoundinpan>gly,
these hotspots contributed more thanpan> half of nutrient losses (52%
of total N losses, anpan>d 62% of total P losses) inpan> this year. The inpan>crease
inpan> hotspot area anpan>d inpan> contributions to total losses inpan> Chinpan>a by hotspots
are also calculated for N anpan>d P losses from various sources inpan> Figures anpan>d 2 anpan>d Table S6.
The spatial
distribution of hotspots also changed between 1990 and 2012. In 1990,
most hotspots are found in the North China Plain, and in the northeastern
part of the Middle and Lower Yangtze River (Figures and 2). In 2012,
the hotspots expanded to cover a larger area of the North China Plain.
Some counties in the AEZs Middle and Lower Yangtze River, Northeast
China, Loess Plateau, Southwest China, and South China also show high
N and P losses as hotspots. In the AEZs where the hotspot areas expanded,
the food production is intensive and increased fast between 1990 and
2012 (Figure S9). The nonhotspots with
low N and P losses (groups I and II in Figures and 2) are found
across northern and western China in 2012, for example, in AEZs Inner
Mongolia and Great Wall Vicinity, Gansu and Xinjiang, and Tibetan
Plateau where food production is less intensive (Figure S9).The result also shows that direct discharge
of animal manure to
waters in the hotspots became a more important source of N and P losses
in food production over the last 30 years. In 1990, the hotspots for
N and P losses to waters from direct discharge of manure only covered
0.2% and 0.3% of the total land. These hotspots were responsible for
less than 10% of the total N and P losses from discharge of manure
(Table S6). However, in 2012 the hotspots
area for direct discharge of manure is calculated to increase by a
factor of 51 for N losses and of 33 for P losses comparing to 1990.
And these hotspots contributed to more than half of N and P losses
(57% for N and 64% for P) from direct discharge of manure in China
(Table S6). This change in N and P losses
from direct discharge of animal manure could be related to the transition
of food production in China started in 1990s (section S1, Figures S6 and S7).[31,32] Traditional-oriented
food production was dominant in the 1990s. Traditional animal systems
are small in size and combined with crop production. Animal manure
is usually used as organic fertilizers for crops. Therefore, the discharge
of manure to waters is relatively low.[33−35] Industrial, highly intensive
systems dominate food production in China since 2000s. Industrial
animal production systems are large in size and are separated from
crop production. The animal manure is usually collected, and discharged
to surface waters or landfills without treatment.[26,31,33−35] Therefore, the discharge
of manure to waters has been increasing since 2000 particularly in
the counties that have intensive animal production activities. Our
results in Figures S6 and S7 indicate that
intensive animal production in the hotspots is considerably higher
than that in nonhotspot counties (P < 0.05). This
situation lasted at least until the ‘Regulation on the Prevention
and Control of Pollution from Large-Scale Breeding of Livestock and
Poultry’ was introduced by Chinese Premier Li,[36] and implemented since 1 January 2014. By introducing this
policy, the Chinese government aims to improve manure management and
manure recycling in order to reduce the environmental pollution caused
by intensive animal production. The “Livestock and Poultry
Manure Utilization Action Program (2017–2020)” was introduced
by MOA[37] in late 2017. As a result, the
direct discharge of animal manure may become smaller in the future.
Our results present the situation in 2012, when policies on manure
management were not widely introduced and not very effective.
Agricultural
and Socio-economic Indicators
Comparing
the hotspots with nonhotspot counties shows that food production in
the hotspots was more intensive than in other counties (Figures and 4, Figures S4 and S5, P < 0.05) in both past (1990) and recent (2012) years. The mean
synthetic fertilizer use and mean animal numbers in the hotspots were
much higher than in other counties in 2012 (Figures and 4, P < 0.05). For example, the mean synthetic fertilizer input in
the hotspots was 400% higher for N, and 300% higher for P than that
in other counties (Table S7). Mean animal
numbers in the hotspots were five times that in other counties in
2012 (Table S7).The N and P use
efficiencies of food production are calculated to be low and their
means are comparable between hotspots and nonhotspot counties (Figures and 4, Figures S4 and S5, P > 0.05). The average nutrient use efficiency of food production
in Chinese counties was 24% for N, and 29% for P in 2012 (Table S7). This is lower than the nutrient use
efficiency in 1990 that was 25% for N and 38% for P (Figures S4 and S5). Howarth et al.[38] estimated the N use efficiency of main crop production at 56% in
United States in 2000. And in Europe the N use efficiency of main
crop production was around 44% in 2000.[39] This indicates that in general the nutrient inputs in food production
in China are not used efficiently because of poor nutrient management
in food production, and as a consequence losses of nutrients to the
air and waters are relatively high. However, the fact that nutrient
use efficiencies in hotspots do not differ from those in nonhotspots,
indicates that low N and P use efficiencies are not the only reason
for the high losses in the hotspots. The high N and P losses are the
net effect of low N and P use efficiencies, intensive food production
(e.g., large animal numbers), and poor nutrient management technologies
(e.g., overuse of synthetic fertilizer, and high direct discharge
of manure to waters as mentioned above) in the hotspots.The
shares of vegetables and fruits in the total sown area in the
hotspots are comparable to that innonhotspot counties (Figures and 4,Figures S4 and S5, P > 0.05). Production of vegetables and fruit was not found to
be
more intensive in hotspots than in other counties. This is surprising
since earlier studies indicate that nutrient losses from fruit and
vegetable production are usually higher than that in other n class="Gene">croppinpan>g
systems as a result of high synpan>thetic fertilizer application.[40−42]
Socio-economic indicators also differ between hotspots and
other
counties (Figures and 4, Figures S4 and S5, P < 0.05). Hotspots are less urbanized.
On average less than 20% of population was urban in the hotspots in
2012 (Table S7). On the other hand, the
meannumber of n class="Species">people workinpan>g inpan> food production inpan> hotspots was three
times that inpan> nonhotspot counties inpan> 2012 (Table S7). The meanpan> inpan>comes for farmers who work inpan> food production
were over 30% higher inpan> the hotspots thanpan> that inpan> the nonhotspot counties
(Table S7). Also the meanpan> total output
value from agriculture anpan>d forestry inpan> these hotspots was considerably
higher (close to five times) thanpan> inpan> other counties inpan> 2012 (Figures anpan>d 4, P < 0.05). Therefore, farmers’
inpan>comes inpan> the hotspots seem to be more dependent on food production.
Discussion
This study is the first to calculate past (1990
and 2000) and more
recent (2012) N and P losses to the environment from food production
in China at the county scale. On this basis we identified the associated
hotspots for N and P losses from food production. We compared several
agricultural and socio-economic indicators for the hotspots with that
for other counties in 2012. The main findings are as follows.We calculate a larger hotspot area for 2012 than for 1990, indicating
N and P losses from food production increased in this period. In 2012,
the hotspots covered 9% of the total land, but were responsible for
52% of total N losses and 62% of total P losses in China. Nutrient
losses from food production in the hotspots are higher than 9625 kg
km–1 for N, and 905 kg km–1 for
P. Note that these losses are even higher than the recommended fertilizer
inputs to arable land. For example, the United Kingdom suggests to
apply 120–270 kg ha–1 N on arable land.[54] Such high losses to the air and waters pose
potentially high risks to the environment. The direct discharge of
animal manure has become an important source of N and P losses in
food production over the last 30 years as the result of industrialization
of animal production in China as indicated in Table S9.The hotspots expanded from part of North China
in 1990 to most
of the area of North China Plain, and some areas of other eastern
AEZs in 2012. Food production in the hotspots are found to be intensive.
Mean synthetic fertilizer use and mean animal numbers in hotspots
were 300–400% higher than that innonhotspot counties in 2012.
N and P use efficiencies of food production were generally low (24%
for NUE, and 29% for PUE) in 2012 and did not differ much between
hotspots and nonhotspot counties. Therefore, low use efficiency of
nutrients is not the only factor to explain the high losses of N and
P in hotspots. The high losses of N and P in the hotspots are the
net effect of the low N and P use efficiencies, and the intensive
food production in these counties. Less than 20% of the population
is urban in hotspots in 2012. The meannumber of n class="Species">people working in
food production in hotspots was three times that in other counties
in 2012. The mean total output value from agriculture and forestry
in hotspots was considerably higher than that in other counties in
2012. Therefore, farmers’ incomes in hotspots seem to be more
dependent on food production than other counties.
Uncertainties
in our analysis are mainly related to the model inputs
and the coefficients that are used in the model calculation. In this
study, we used county data from the Chinese statistical year book
as inputs to NUFER. Some of the model inputs were
missing in this data set. For the incomplete information, the provincial
data from Chinese statistical year book and China livestock yearbook
were used as complement (see section S2 in the Supporting Information for more details). These statistical
yearbooks are known to be the most reliable data source in China.
The model coefficients (e.g., nutrient content in crops, nutrient
loss factors) are from the original NUFER model,
which was taken from other peer reviewed papers and interviews of
farmers in China.[28] Our results are comparable
with other studies. For example, we estimated comparable NH3 losses from food production to the air, and N and P losses to waters
at the national scale with many of the other studies (Table ). Our estimates of N2O emission in 2012 is lower than in the other studies (Table ). A likely explanation is that
N2O emission from the burning of straw was not considered
in our study. This can be improved in our model. However, we do not
think this will lead to large change in our conclusions since N2O losses are minor compared to other losses of reactive N
from food production (Figures and 2). Huang et al.,[55] Kang et al.,[56] Zhang et al.[57] calculated similar spatial distribution patterns
for NH3 emissions as we did. The study of Gu et al.[48] identified similar spatial distribution of NH3 and N2O emissions from food production and other
human activities and calculated a considerable increase in these emissions
between 1990 and 2010. Similar spatial distribution of P losses from
food production and other sectors in 2012 was also identified by Liu
et al.[53]
Table 1
Comparison of N and
P Losses (Tg year–1) to the Air and Waters from
Food Production Including
Crop and Animal Production in China by Our Study with Estimates by
Other Published Studies
N or P losses
(Tg year–1)
studies
system boundary
2000
2006
2007
2008
2010
2012
NH3
This
study
food production
11.3
13.5
Huang et al.[43]
food production
9.2
Gu et al.[44]
food production
12.3
Crippa et al.[45]
food production
9.5
12.5
Dianwu and Anpu[46]
food production (76%) +
other
13.6
Streets et al.[47]
food production (80%) +
other (20%)
13.6
Gu et al.[48]
food production (88%) +
other
11.2
Ti et al.[49]
food production + other
10
9.8
Cui et al.[50]
food production + other
10
Kurokawa et al.[51]
food production (80%) +
other (20%)
12.5
14.3
14.8
N2O
This study
food production
0.4
0.4
Zhou et al.[52]
food production
1.4a
Gu et al.[44]
food production
0.4
Crippa et al.[45]
food production
0.6
0.9
Gu et al.[48]
food production (45%) +
other
1.1
Cui et al.[50]
food production + other
0.4
Total
N to waters
This study
food
production
9.2
12.8
Gu et al.[44]
food production
7.8
Strokal et al.[32]
food production (six river
basins)
8.5
Ti et al.[49]
food production + other
8.8
9.3
Cui et al.[50]
food production + other
12
Total
P to waters
This study
food
production
1.5
2.5
Strokal et al.[32]
food production
1.3
Liu et al.[53]
food production + other
1.7
This study accounts
for nitrous
oxide (N2O) emissions from the burning of straw (0.5),
which we do not consider. However, our results shows similar spatial
patterns to those in this study.
This study accounts
for nitrous
oxide (n class="Chemical">N2O) emissions from the burninpan>g of straw (0.5),
which we do not consider. However, our results shows similar spatial
patterns to those inpan> this study.
Opportunities
to Reduce N and P Losses
Our results
show that hotspots contributed to more than half of total N and total
P losses from food production in China, while covering less than 10%
of the country area in 2012. Thus, it is important to reduce the nutrient
losses from food production in these hotspots in order to control
nutrient pollution in China. Region-specific nutrient management needs
to be developed for the hotspots particularly for the North China
Plain where N and P losses are relatively high, and the food production
activities are more intensive than other regions. By comparing the
socio-economic indicators in hotspots and nonhotspots, we found that
farmers’ incomes in hotspots are more dependent on food production.
Therefore, the production of food in the hotspots needs a transformation
in order to avoid negative effects of pollution on the economies of
local societies.There are many technical improvements possible
to reduce losses of nutrients. In crop production, techniques that
help to fertilize crops based on their specific needs could reduce
N and P inputs to land by up to 20%, and improve the associate nutrient
use efficiencies in the hotspots.[13,58−60] Increasing farm sizes may also decrease the use of synthetic fertilizer
in crop production.[61] Nutrient losses to
the air and waters could be reduced by up to 60% for N, and 85% for
P without reducing crop yields, needed to meet the increasing food
demand in China.[60] This also secures the
interests of farmers since their incomes are dependent on food production.
Low-emission technologies (e.g., inject animal manure into soil) could
be adopted to reduce the NH3 and N2O emission
from applying fertilizers on cropland.[26,62,63] Experimental and modeling studies[58,59,64,65] that explore
and implement the above-mentioned techniques in the North China Plain
could be used as a good basis for the nutrient management in hotspots.
The intensive industrial animal production leads to large N and P
losses to the environment in the hotspots. Technologies that improve
the quality of animal feed could reduce 20–30% of the N and
P excretion,[39,66,67] thus reduce the overall N and P losses from animal production. The
N and P losses to waters from direct discharge of manure could be
much reduced by up to 85% via recycling animal manure on cropland
as organic fertilizers and by improving the treatment of animal manure
before discharging.[60,68] The N and P losses to the air
from animal production can be reduced in several ways, for instance
by shifting to low emission housing and manure storage technologies
that are currently used in some European Union (EU) countries.[69] The above nutrient management options are technically
effective in reducing N and P losses. In future analyses, it is important
to also explore whether these options are economically affordable,
because implementation of these options may be a challenge in China.In summary, our study can be used to identify hotspot counties
where pollution control technologies are needed to reduce N and P
losses from food production. This holds in particular for technologies
to reduce synthetic fertilizer use, to improve nutrient use efficiencies
of food production, to reduce emission of N in animal housing and
manure storage, and to increase recycling of manure on land. The challenge
will be to secure food production so that farmers’ interests
are not negatively affected by pollution control.
Authors: Lex Bouwman; Kees Klein Goldewijk; Klaas W Van Der Hoek; Arthur H W Beusen; Detlef P Van Vuuren; Jaap Willems; Mariana C Rufino; Elke Stehfest Journal: Proc Natl Acad Sci U S A Date: 2011-05-16 Impact factor: 11.205
Authors: Shenghui Cui; Yalan Shi; Peter M Groffman; William H Schlesinger; Yong-Guan Zhu Journal: Proc Natl Acad Sci U S A Date: 2013-01-22 Impact factor: 11.205
Authors: Waqas Ahmed; Huang Jing; Liu Kaillou; Muhammad Qaswar; Muhammad Numan Khan; Chen Jin; Sun Geng; Huang Qinghai; Liu Yiren; Liu Guangrong; Sun Mei; Li Chao; Li Dongchu; Sehrish Ali; Yodgar Normatov; Sajid Mehmood; Huimin Zhang Journal: PLoS One Date: 2019-05-10 Impact factor: 3.240
Authors: Xi Chen; Maryna Strokal; Michelle T H Van Vliet; John Stuiver; Mengru Wang; Zhaohai Bai; Lin Ma; Carolien Kroeze Journal: Environ Sci Technol Date: 2019-08-01 Impact factor: 9.028