Institutionen för naturgeografi
Examensarbete grundnivå
Hydrologi och hydrogeologi, 15 hp
Comparing C, N and P concentrations in soils in agricultural versus natural
land, and across climates
Agnes Classon
HG 16
2016
Förord
Denna uppsats utgör Agnes Classons examensarbete i Hydrologi och hydrogeologi på grundnivå vid Institutionen för naturgeografi, Stockholms universitet. Examensarbetet omfattar 15 högskolepoäng (ca 10 veckors heltidsstudier).
Handledare har varit Stefano Manzoni, Institutionen för naturgeografi, Stockholms universitet. Examinator för examensarbetet har varit Steve Lyon, Institutionen för naturgeografi, Stockholms universitet.
Författaren är ensam ansvarig för uppsatsens innehåll.
Stockholm, den 13 juni 2016
Steffen Holzkämper
Chefstudierektor
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Abstract: How do concentrations of C, N and P vary between agricultural and natural land?
How do C, N and P concentrations vary between climate zones? Soil organic carbon (SOC), total nitrogen (TN) and phosphorus (TP) as well as microbial C, N and P (MBC, MBN and MBP respectively) concentrations in soils were collected through a literature review, and studied to analyze the differences between agricultural land-use and natural land, and between different climate zones. The minimum concentrations of SOC, TN, MBC, MBN and MBP were found in the agricultural soils and the maximum concentrations in natural soils. The minimum TP concentration was the same for the two land types but the maximum concentration was found in agricultural soils. The mean concentrations of MBC, MBN, MBP, SOC and TN were significantly lower in the agricultural land than in the natural land.
The highest concentrations of soil and microbial C, N and P were found in the tropical wet climate, in the highlands, in the midlatitude climate with high temperature variations, and in the marine west coast climate. The results show that: 1. rainfall and mild to warm temperatures could increase nutrient concentrations; 2. northern latitudes and highlands have high stocks of nutrients, and 3: Humid subtropical climates are probably more exploited to humans due to agricultural productivity which decreases nutrient concentrations.
The results clearly show the loss of nutrients following cultivation, and the importance of
research of nutrient status in soils; for global soil and water quality issues, for a sustainable
agricultural production and for ecosystems.
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Table of Contents
Introduction ... 5
Material and Methods ... 6
Data collection and processing... 6
Statistical analysis ... 8
Results ... 8
Soil microbial biomass concentrations in agricultural and natural land ... 8
Soil concentrations in agricultural and natural land ... 9
Soil microbial biomass concentrations of different climates ... 13
Soil concentrations in different climates ... 13
Discussion ... 16
Soil and microbial concentrations in agricultural and natural land ... 16
Soil and microbial concentrations in different climate areas ... 16
Uncertainties and future research ... 17
Conclusions ... 17
Acknowledgment ... 18
References ... 18
Appendix ... 21
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Introduction
Soil organic matter (SOM) and soil microbial biomass (SMB) are important parts of the carbon, nitrogen and phosphorus cycles on earth. Microbial biomass uses soil organic carbon, nitrogen and phosphorus in animal and plant residues in the decomposition process (Rousk and
Bengtson, 2014). Decomposition of organic matter returns nitrogen and phosphorus to the soil, which will be available for plant uptake in their growing process, as well as releasing CO
2to the atmosphere (Eldor, 1989).
The amount of C, N and P that enters the soil depends on the vegetation above it; the amount and type of litter that will fall on the ground and reach the soil (FAO, 2015). Since
decomposition is a slow process, large amounts of these nutrients will be stored in the soils, referred to as the soil organic carbon (SOC), total nitrogen (TN) and total phosphorus (TP).
Batjes (2014) estimated the amount of stored SOC in terrestrial systems of the world to be about 2400 Pg for the top 200 cm soil layer.
The clearing and conversion of native forests and grasslands into crop production results in severe losses of SOC, TN and TP, as well as microbial biomass C, N and P (Celik, 2005; Gupta and Germida, 1988; Srivastava, 1989). Large pools of carbon and nitrogen may be released from the soil and emitted to the atmosphere as greenhouse gases (CO
2and N
2O), and some nutrients may follow runoff and result in increased concentrations in lakes and seas (Davidson and Ackerman, 1993; Rankinen, 2016; Tilman, 2014). Furthermore, loss of nutrients from soils results in a degradation of the soil quality which could enhance soil erosion (Jones, 2015;
McDonald, 2002; Quijano et al., 2015). Bai et al. (2008) estimated that 24 % of the world’s land areas are degrading.
In order to restore and improve the soil status after clearance and increase crop production, fertilizers of nitrogen and phosphorus are added to the soil. Unfortunately, a large amount of fertilizers are not taken up by the growing plants but will add to the already higher
concentrations of nitrogen and phosphorus in water bodies and in the atmosphere (Bouwman, 2009; Rankinen, 2016). Larger concentrations of phosphorus and nitrogen in water results in eutrophication, algae blooming and oxygen depletion (Diaz and Rosenberg, 2008), a common issue in the world, where one is the Baltic Sea.
An increasing population with a higher demand for food production will result in intensified agriculture and clearance of more natural land, which will in turn add to the loading of nutrients in the seas and to the greenhouse gases in the atmosphere. In order to find possible causes and solutions for global water quality issues, it is therefore important to understand the amount of nutrients and carbon stored in the soil as soil organic matter.
A consequence of rising temperatures due to higher inputs of greenhouse gases is the altering of climate and rainfall patterns, which is already seen (Dore, 2005). High precipitation enhances nutrient runoff and soil erosion (Giertz, 2010; McDonald, 2002). Hence, a variation in rainfall patterns may affect the amount of soil organic matter in the soils and the nutrient flows between terrestrial and marine ecosystems.
Although agriculture has negative consequences for the soil, the atmosphere and water
ecosystems, research has found that long-term crop production may increase soil organic carbon concentrations and function as a carbon stock (Pan, 2010). The amount of carbon the soil can store depends on precipitation, soil quality, the type of crop that is planted and grown, and the amount of plant residues that enters the soil (McCarl, 2007). Hence, it is important with a large amount of microbial biomass in order to keep high plant productivity.
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The importance of SOC, TN and TP as well as microbial biomass C (MBC), N (MBN) and P (MBP) in global soils in order to minimize ecological impacts is clear. Here, the focus is on the impact on these nutrients from agriculture and different climate.
There is a lot of research on SOC, TN, TP and microbial biomass C, N and P concentrations in soils across the world (e.g. Pan, 2010; Srivastava, 1990; Xu, 2013), but focus is on a regional scale or on a specific biome or land use, or only SMB or SOC. In this paper, the main objective was to study both SMB and total soil nutrients (SOC, TN and TP) on a global scale; comparing different biomes and impacts from agricultural use on nutrients versus natural land. Also, the effect of different climate on the nutrient concentrations would be included. Two questions were in focus for the study:
1. How does the soil C, N and P concentrations vary between agricultural and natural land?
2. How does the soil C, N and P concentrations vary between different climate areas?
Material and Methods
Data collection and processing
This work was conducted through a literature review and data collection of already existing measurements of microbial biomass carbon (MBC), microbial biomass nitrogen (MBN) and microbial biomass phosphorus (MBP) in the soil, as well as quantities of soil organic carbon (SOC), total soil nitrogen (TOC) and total soil phosphorus (TOP).
Data was collected by searching through the ISI Web of Science online database
(http://isiknowledge.com/), through a dataset from a literature review constructed by Xu et al.
(2013) and through a dataset constructed by Li et al. (2012). Search terms for the Web of Science were e.g. “nutrients + agriculture”, “nutrients and land-use” and “C, N and P in soils”, all of which gave several hundred returns. To narrow down the search, two conditions were set for the data collection: 1) both microbial C, N and P and soil C, N and P must be reported, and 2) only data from the top soil layer will be used. Since different studies had different depths of the top soil layer, the resulting data collection had top soil layers varying from 0-5 cm to 0-30 cm. From the study made by Li et al., (2012), the 10 first data points from each landscape and land-use type were obtained to make it equally representative in amount of data as the other studies.
The final dataset consisted of 437 data points (Appendix) from all over the world (figure 1).
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Figure 1: Spatial disitribution of data points. The blue ring in the bottom right corner shows New Zeeland. Map taken from Google (2016).
In excess of soil nutrient data and sampling depth, other information was collected, such as geographical coordinates, mean annual precipitation (MAP), biome type and land use.
In some studies, the MAP was reported as a range (e.g. MAP of 700-1000 mm). In those cases an average was calculated. A study in New Zealand (Sparling et al., 1994) did not have a reported MAP. This was filled in by obtaining rainfall data of a nearby city from another study (Graham, 2003).
Since most of the data were reported in mmol/kg it was decided to be the common unit for the data-set in this study. Conversions had to be performed whenever another unit was used (e.g.
from mg/g to mmol/kg) by using the known atomic weights of C, N and P (in g/mol).
For the first analysis, data was divided into two different land-uses: agriculture and natural land.
This was done by following the already reported land-use or through the description of the land- use in the original study. Agricultural land was defined as “land used or changed by humans for crop production, agroforestry or grazing”. Natural land was defined as “land not changed due to human activity”.
For the second analysis, data was divided into climate zones following the Köppen System (McKnight, 1999). Reported geographical coordinates or reported names of cities were obtained and used in Google Maps (Google, 2016) to connect the research area to its specific climate zone. Most of the data belonged to the climate zone C (humid subtropical, mild latitude), so it was decided that these should be further divided into the subtypes Cfa, Cfb and Cwa. The final climate zones were A (tropical humid), B (extreme contintental;
evapotranspiration > precipitation), Cfa (hot summers without dry season), Cfb (warm summers
without dry season), Cwa (hot summers and dry winters), D (midlatitudes with high temperature
variations) and H (highland). The descriptions of the climate zones are shown in table 1.
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Table 1: Mean annual precipitation, mean monthly temperature and descriptions of the different climate zones.
Climate information from McKnight (1999).
Climate type Letter
Mean annual precipitation
(mm)
Mean monthly
temperature (°C) Climate patterns
Tropical humid A 900-5008 >18 No dry season or distinct
dry and wet season
Extreme continental B 250-630 NA Cold winters and hot
summers
Humid subtropical Cfa/Cwa 650-2500 >5 Hot summers, mild
winters
Marine west coast Cfb 750-1250 2 - 21 Mild winters and
summers
Midlatitudes D 120-1250 -12 - 20 Hot summers, cold
winters
Highlands H NA Elevation dependent Climate depends on the
surrounding climate zone
Statistical analysis
The data had a high spread with many extreme values. To normalize the data and make it more suitable for statistical analysis, a log-transformation was performed.
Statistical analysis was performed in Matlab (Matlab R2015b, 2013). Data were tested for significant differences between the two land-uses agriculture and natural, as well as for the climate zones, A, B, Cfa, Cwa, Cfb, D and H, using ANOVA (anova1 in Matlab2013). The significance level was set to be over the 95 % confidence level, where p < 0.05.
Results
Soil microbial biomass concentrations in agricultural and natural land The soil microbial biomass carbon concentration varied from 3.90 to 1508 mmol kg
-1with lower values in the agricultural soil (table 1, figure 1). The soil microbial biomass nitrogen concentration varied from 0.40 to 122 mmol kg
-1, and the microbial biomass phosphorus concentration varied from 0.03 to 17.6 mmol kg
-1. Both of the lowest microbial biomass N and P concentrations were found in the agricultural soil but the highest N and P concentrations were found in the natural soil (table 1, figure 1). The mean MBC, MBN and MBP concentrations were all lower in the agricultural soils than in the natural soils.
The standard deviation and the skewness are large, showing the variation in concentrations of C, N and P, although the concentrations are less variable in the agricultural soils (table 1). The skewness is lower in the agricultural soils showing a more normal distribution.
The minimum, maximum and the mean MBC, MBN and MBP concentrations, as well as the
standard deviation and the skewness of the concentrations, in agricultural and natural soils are
summarized in table 1.
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Table 2: Minimum, maximum and average MBC, MBN and MBP concentrations in agricultural and natural land. Also shown is the standard deviation (StD) and skewness of the MBC, MBN and MBP concentrations
for each land-use.
MBC
(mmol kg-1)
Land-use Minimum Maximum Average StD Skewness
Agriculture 3.90 365 46.9 59.9 3.52
Natural 8.27 1508 94.4 172 5.30
MBN
(mmol kg-1)
Land-use Minimum Maximum Average StD Skewness
Agriculture 0.40 32.8 4.17 3.93 3.21
Natural 0.67 122 10.2 15.5 4.47
MBP
(mmol kg-1)
Land-use Minimum Maximum Average StD Skewness
Agriculture 0.03 4.68 0.68 0.76 2.95
Natural 0.12 17.6 1.72 2.19 3.53
The range of the microbial biomass nitrogen and phosphorus concentrations is larger where the land is not used for agriculture (figure 1). The MB carbon, nitrogen and phosphorus
concentrations in the soils of agricultural land-use are lower as well as the median of the concentrations which is seen in figure 1. The microbial biomass nitrogen and phosphorus in soils of natural land show larger variations than the microbial biomass carbon in the same land- use.
The statistical analysis showed significant differences of the means of microbial biomass carbon, nitrogen and phosphorus concentrations between agricultural land-use and natural land- use with all p-values lower than 0.05.
Soil concentrations in agricultural and natural land
The soil organic carbon concentration varied from 217 to 43045 mmol kg
-1. The total nitrogen concentration varied from 13.6 to 2064 mmol kg
-1and the total phosphorus concentration varied from 3.23 to 100 mmol kg
-1. The minimum concentrations of soil organic C and total N were all found in the agricultural soils but in both natural and agricultural soils for total P (table 2). The maximum C and N concentrations were found in the natural soils but the maximum P
concentration in the agricultural soils. The agricultural soils had lower mean SOC and TN concentrations but higher mean TP concentrations than in natural soils.
The standard deviation and the skewness are large also in the concentrations of SOC, TN and TP, here again showing the large variations of carbon, nitrogen and phosphorus in the soils.
Although, the variations are much larger in the natural soils for carbon and nitrogen (table 2) but
less for phosphorus in the natural soils. The skewness is lower in the natural soils showing a
more normal distribution.
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The minimum, maximum and the average SOC, TN and TP concentrations in agricultural and natural soils are summarized in table 2, as well as the standard deviation and the skewness.
Table 3: Minimum, maximum and average soil organic C, total N and total P concentrations in agricultural and natural land. Also shown is the standard deviation (StD) and skewness of the C, N and P
concentrations for each land-use.
Soil organic C
(mmol kg-1)
Land-use Minimum Maximum Average StD Skewness
Agriculture 217 10142 1607 1409 3.32
Natural 408 43045 5359 8357 3.06
Total N
(mmol kg-1)
Land-use Minimum Maximum Average StD Skewness
Agriculture 13.6 789 141 103 3.03
Natural 21.4 2064 320 380 2.63
Total P
(mmol kg-1)
Land-use Minimum Maximum Average StD Skewness
Agriculture 3.23 229 25.5 24.9 4.33
Natural 3.23 100 19.6 13.7 1.97
The range of the soil total carbon and total nitrogen concentrations is larger where the land is not used for agriculture (figure 2). Also, the extreme values are higher in the natural land for SOC and TN but higher in the agricultural land for TP. The concentrations of SOC and TN are generally lower in the agricultural soils but the TP concentration is higher in this land-use. The agricultural soils have more extreme values (outliers). The median concentrations are higher in natural soils for the SOC and TN but lower for the TP concentration, which is shown in figure 2.
The statistical analysis showed significant differences of the means of soil organic carbon, total
nitrogen and phosphorus concentrations between agricultural land-use and natural land-use with
all p-values being lower than 0.05 (3.40 e
-18, 2.21 e
-13and 0.0013 respectively)
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Figure 2: Range of concentrations of microbial biomass carbon (MBC), nitrogen (MBN) and phosphorus (MBP) in soils of agricultural land (left column) and natural land (right column). The median value of concentrations is shown as a dot inside the boxes. Dots
outside the vertical bars are extreme values.
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Figure 3: Range of concentrations of soil organic carbon (SOC), total nitrogen (TN) and total phosphorus (TP) in soils of agricultural land (left column) and natural land (right
column). The median value of concentrations is shown as a dot inside the boxes. Dots outside the vertical bars are extreme values.
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Soil microbial biomass concentrations of different climates
The concentrations of microbial biomass carbon and phosphorus show the largest variations in the climate areas of D and H, and also in A for nitrogen (figure 3). The highest concentrations of MBC, MBN and MBP are found in the climate zones of D and A, but Cfb and H also show high concentrations. The lowest concentrations of MBC, MBN and MBP are found in climate Cfa, Cwa and in H. The extreme continental climate of B shows generally high concentrations. The median concentrations of MBC, MBN and MBP are lowest in the climate area of H. The distribution of soil microbial biomass carbon, nitrogen and phosphorus concentrations for the different climate areas is shown in figure 3.
The statistical analysis gave p-values of <0.05 for MBC, MBN and MBP for all the climate zones, showing significant differences of the means of concentrations between climate areas.
The mean concentrations in the climate zone D are significantly different from the others except for in MBP where it overlaps with cimate zone B. The mean concentrations of climate zone Cfa and Cfb are significantly different for all MBC, MBN and MBP. A and Cfb show similar means and Cfa, Cwa and H overlap for all the MBC, MBN and MBP.
Soil concentrations in different climates
The distribution of soil organic carbon and total nitrogen concentrations is larger in the climate zones of H but is less variable for total phosphorus (figure 4). The lowest concentrations of SOC and TN are found in H but in Cwa for TP. The highest concentrations of SOC, TN and TP are found in climate A and D, but Cfb and H also show generally high concentrations. The extreme continental climate B shows lower C, N and P concentrations than the humid subtropical climates of Cfa/Cwa. The median concentrations of SOC and TN are lowest in H but in D for TP, and the highest median concentrations are found in D for all the SOC, TN and TP. The distribution of soil organic carbon, total nitrogen and total phosphorus in the different climate areas is shown in figure 4.
The statistical analysis showed significant differences of SOC, TN and TP between the different
climate areas with all the p-values being lower than 0.05. The mean concentrations in the
climate zone D is significantly different from the others for SOC and for TN (but overlaps with
A). B, Cfa, Cwa and H overlap for SOC and TN as well as A and Cfb. For TP B and Cwa differ
significantly from all the others which overlap.
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Figure 4: Range of concentrations of MBC, MBN and MBP in soils of different climate zones (A, B, Cfa, Cfb, Cwa, D and H respectively in columns). The median value of concentrations is shown as a dot inside the boxes. Dots outside the vertical bars are
extreme values (outliers).
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Figure 5: Range of concentrations of soil organic carbon (SOC), total nitrogen (TN) and total phosphorus (TP) in soils of different climate
zones (A, B, Cfa, Cfb, Cfa, D, H in columns). The median value of concentrations is shown as a dot inside the boxes. Dots outside the
vertical bars are extreme values (outliers).
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Discussion
Soil and microbial concentrations in agricultural and natural land
The difference of soil organic C, N and P as well as soil microbial biomass C, N and P
concentrations between agricultural land and natural land (table 1 and 2, fig. 1 and 2) agree well with other studies. Xu et al. (2013) found that the concentrations of SOC, TN, TP and microbial biomass globally, were lower in cropland and in pasture in almost all cases compared to natural land. Pan et al. (2010) and Srivastava and Singh (1990) investigated soils in China and India.
They found lower concentrations of SOC, TN, TP and microbial biomass in croplands, and in grasslands and savannahs converted from forests.
The lower carbon, nitrogen and phosphorus concentrations in agricultural land could be an effect of the decreased biomass of the cultivated soil compared to natural forests where the biomass is very high. A high amount of biomass results in high concentrations of organic matter which stabilizes the soil and prevents erosion and loss of nutrients (FAO, 2015). Also,
cultivated soils are cleared regularly which results in less nutrients returning to the soil (Srivastava, 1990).
The slightly higher P (TP) concentrations in agricultural soils compared to natural soils (table 2, fig. 2) also agree with other results. Xu et al. (2013) found higher P concentrations in
agricultural land than in temperate and tropical forests but lower than in tundra, natural wetlands and grasslands. This could be an effect of added fertilizers in agricultural soils. Li et al. (2012) found increased concentrations of phosphorus in agricultural soils with increased inputs of fertilizers.
The relatively high C, N and P concentrations in natural land compared to agricultural land are not only because of nutrient losses during cultivation and crop production. Soils in northern latitudes have in general, very high concentrations of C and N (Xu et al., 2013) which is also shown in this study (appendix A).
Soil and microbial concentrations in different climate areas
There is little research about soil and microbial concentrations and the relationship with water availability and temperature. Xu et al. (2013) compared different biomes and found that boreal forests and tundra, which are found in the northern latitudes, had higher nutrient concentrations than more southern biomes, such as tropical forests. This agrees with the concentrations in this study, where the climate zone D with humid continental and subarctic climate shows generally higher C, N and P concentrations than the other climate areas.
Kang et al. (2010) analyzed leaf litter nutrient concentrations and the connection with MAP and MAT. They found that N concentrations increased with MAT and MAP, but decreased with latitude. These results agree partly with the results in this study. The nitrogen concentrations are generally higher in the tropical humid climate (A) and in the mild midlatitudes/marine west coast (Cfb) which agrees with the study of Kang et al. (2010). But high N concentrations are also found in the climate zones of D (very cold and warm) and H (mountainous) (figure 2 and 3). A reason for this could be that these climate zones (D and H) are still much unexploited to human activity and a large amount of N is stored in the soil. Also, the cold climate could slow down the decomposition process and keep a high amount of N in the soil.
The concentrations in climate zones Cwa and Cfa are more equally distributed than the other climate areas which could be an effect of the similar climate patterns. The nutrient
concentrations in Cfb are higher than the other two in the same main climate zone. This may be
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due to different climate, no dry seasons and more mild temperatures in the summer than Cfa, which may enhance the plant growth and decomposition process. Also, Cfa and Cwa could be more exploited to human activity and agricultural production which causes a loss of nutrients.
The nutrient concentrations in climate zone A have very similar distributions to the concentrations in Cfb which may be due to similar rainfall and temperature patterns; very short/short or no dry season and mild/warm temperatures which do not vary so much throughout the year.
The climate zone of B shows low concentrations of SOC, TN and TP which could be explained by the dry climate with little biomass. The high microbial biomass could be explained by the low precipitation which could infiltrate the soil and enhance nutrient runoff. The high biomass could also be an effect of the low biomass predator activity due to low rainfall. This will lead to a lower death rate than growing rate and hence, more biomass (Parker and Schimel, 2011).
The large distribution of concentrations in the climate zone H could be due to the spatial variation. The high elevation is the controlling climatic factor but the low areas surrounding the mountains, which define the climate of the mountains, could belong to different climate zones depending on the geographic position. Also, mountainous areas are not used much by humans for agriculture so the controlling factor of the nutrient concentrations is probably the geographic position and the soil type.
Uncertainties and future research
The collected data has measurements from different months and different years which may result in variations of nutrient concentrations. Some data are quite old and some are new which could affect the results; soils from 10 or 20 years ago may not hold the same amount of nutriens as of today. The type and amount of fertilizers are different which could affect the results, as well as the amount and type of plants grown. A time limit should be considered, natural soils may have been agricultural soils at some time and hence, the soil structure is still different.
Even though the definition of agriculture in this thesis was considered as “land changed by humans” there is not always a use of fertilizers. Also, agricultural practices differ from land to land and between countries. Both should be considered in future research.
Climate varies a lot globally; more data from each climate zone could be good. Also, the amount of data is not equally distributed between the climate zones and the agricultural and natural soil comparisons. Climate zone C had a lot of data and climate zone B and D had very few. A larger data collection with monthly or seasonal measurements and a larger and more specific spatial variability would result in an even better presentation of differences globally.
Also, an equal time range for all the data would be needed to find changes over time.
Conclusions
This study shows statistical differences of C, N and P concentrations between agricultural land- use and natural soils. All the concentrations of MBC, MBN and MBP were lower in the
agricultural soils, as well as SOC and TN. The total phosphorus concentrations are lower in the agricultural land, although, the difference between agricultural and natural soils is not as high as for the other nutrients.
Northern latitudes have a high stock of microbial biomass C, N and P and SOC, TN and TP; the
dry climate (B) has low concentrations of SOC, TN and TP.
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Tropical humid (A) and humid subtropical, mild latitude (C) climate have less extreme concentrations even though they have higher temperatures and more precipitation. This is probably due to more agricultural use. The effect of climate on carbon, nitrogen and phosphorus concentrations is clear, showing several climate zones significantly different from each other.
Rainfall and temperature have a large impact on soil concentrations which should be taken into account in agricultural production.
The difference of nutrient concentrations between agricultural and natural land is clear, as well as the effect of climate and land-use. The storage and loss of nutrients in soils are important for future agricultural planning and production but also for global water issues, soil quality and ecosystems.
Acknowledgment
I would like to thank Xiaofeng Xu for letting me use his data set. Also, I would like to thank my supervisor, Stefano Manzoni for all the help and guidance.
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Environment, vol. 213, pp. 194-208.
Rankinen, K., Keinänen, H. & Bernal, J.E.C. 2016, "Influence of climate and land use changes on nutrient fluxes from Finnish rivers to the Baltic Sea", Agriculture, Ecosystems & Environment, vol. 216, pp.
100-115.
Salem, B. 1989, Arid zone forestry: a guide for field technicians. Food and Agriculture Organization (FAO).
Singh, J., Singh, D. & Kashyap, A. 2010, "Microbial biomass C, N and P in disturbed dry tropical forest soils, India", Pedosphere, vol. 20, no. 6, pp. 780-788.
Srivastava, S. & Singh, J. 1989, "Effect of cultivation on microbial carbon and nitrogen in dry tropical forest soils", Biology and Fertility of Soils, vol. 8, no. 4, pp. 343-348.
20
Srivastava, S. & Singh, J. 1991, "Microbial C, N and P in dry tropical forest soils: Effects of alternate land-uses and nutrient flux", Soil Biology and Biochemistry, vol. 23, no. 2, pp. 117-124.
Xu, X., P.E. Thornton, and W.M. Post. 2014. A Compilation of Global Soil Microbial Biomass Carbon, Nitrogen, and Phosphorus Data. Data set. Available on-line [http://daac.ornl.gov] from Oak Ridge
National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA.http://dx.doi.org/10.3334/ORNLDAAC/1264
21
Appendix
Data of MBC, MBN, MBP and SOC, TN and TP used in this study. Each digit in the reference column is referred to as following:
1Xu, X., P.E. Thornton, and W.M. Post. 2014. A Compilation of Global Soil Microbial Biomass Carbon, Nitrogen, and Phosphorus Data. Data set. Available on-line [http://daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee,
USA.http://dx.doi.org/10.3334/ORNLDAAC/1264
2Li, Y., Wu, J., Liu, S., Shen, J., Huang, D., Su, Y., Wei, W. & Syers, J.K. 2012, "Is the C: N: P stoichiometry in soil and soil microbial biomass related to the landscape and land use in southern subtropical China?", Global Biogeochemical Cycles, vol. 26, no. 4.
3Singh, J., Singh, D. & Kashyap, A. 2010, "Microbial biomass C, N and P in disturbed dry tropical forest soils, India", Pedosphere, vol. 20, no. 6, pp. 780-788.
4Srivastava, S. & Singh, J. 1991, "Microbial C, N and P in dry tropical forest soils: Effects of alternate land-uses and nutrient flux", Soil Biology and Biochemistry, vol. 23, no. 2, pp. 117-124.
Country Biome Microbial biomass
(mmol/kg)
Soil nutrients (mmol/kg)
Referenc e
MBC MBN MBP SOC TN TP
Sweden Shrub 1158
100.
0 13.2
4161 0
162
9 38.1 1
Sweden Tundra 1508
100.
0 1.6
4165 6
137
9 81.0 1
United States
of America Tundra 379 23.9 0.8
1786
0 782 11.8 1
New Zealand Grassland 41.5 7.0 0.7 1708 110 15.8 1
New Zealand Grassland 30.6 5.4 0.4 1250 87.1 15.2 1
New Zealand Grassland 33.3 7.2 0.5 1358 99.3 16.1 1
Germany Cropland 10.8 1.2 0.2 542 39.3 12.6 1
Germany Cropland 13.3 1.6 0.4 633 45.0 16.1 1
Germany Cropland 10.8 1.2 0.3 567 44.3 13.2 1
Germany Cropland 11.7 1.4 0.3 600 43.6 13.5 1
Germany Cropland 11.7 1.1 0.2 617 43.6 13.2 1
Germany Cropland 10.8 1.3 0.3 600 43.6 12.6 1
Germany Cropland 12.5 1.4 0.3 683 49.3 13.9 1
Germany Cropland 12.5 1.4 0.3 667 50.0 13.2 1
Germany Cropland 11.7 1.5 0.5 758 67.1 12.6 1
Germany Cropland 14.6 2.1 0.7 967 88.6 14.5 1
Germany Cropland 16.9 2.5 0.6 1017 81.4 14.2 1
Germany Cropland 14.3 2.0 0.5 908 77.9 13.9 1
Germany Cropland 14.5 2.0 0.7 925 80.0 13.2 1
United States
of America Natural Wetland 625 72.8 5.1
3491 7
179
3 43.7 1
United States
of America Natural Wetland 1000 122 7.6
3141 7
176
4 22.8 1
United States Natural Wetland 750 64.1 2.4 2983 191 9.8 1
22
of America 3 4
United States
of America Natural Wetland 841 24.5 5.8
3266 7
206
4 32.3 1
United States
of America Natural Wetland 287 31.4 2.6
3233 3
202
9 32.3 1
Sweden Boreal Forest 335 40.1 10.6
4304
5 914 28.1 1
Sweden Boreal Forest 309 38.3 17.6
4271 2
104
2 31.3 1
Sweden Boreal Forest 298 32.9 9.6
4279 5
113
5 29.4 1
Pakistan Cropland 5.1 0.4 0.1 217 13.6 15.8 1
Pakistan Cropland 6.0 0.4 0.1 217 14.3 18.4 1
Pakistan Cropland 7.5 0.6 0.1 266 20.0 16.8 1
Pakistan Cropland 8.0 0.7 0.1 291 20.7 20.3 1
Pakistan Cropland 12.4 1.0 0.2 350 24.3 18.7 1
Pakistan Cropland 8.4 0.8 0.1 266 20.0 26.2 1
Pakistan Cropland 8.6 0.8 0.1 291 22.8 17.4 1
Pakistan Cropland 9.7 0.8 0.1 316 20.7 19.7 1
Pakistan Cropland 15.7 1.4 0.2 425 34.3 22.3 1
Pakistan Cropland 10.5 1.0 0.1 358 26.4 18.1 1
Pakistan Cropland 16.5 1.6 0.2 533 40.0 23.9 1
Germany Cropland 12.4 1.9 0.2 842 66.4 21.9 1
Germany Cropland 15.1 2.5 0.2 892 71.4 31.9 1
Germany Cropland 9.3 1.4 0.2 1292 104 61.0 1
Germany Cropland 12.3 1.9 0.3 1092 104 37.1 1
Germany Cropland 53.7 6.9 1.0 4650 355 161.3 1
Germany Cropland 35.1 4.8 0.5 2567 209 229.4 1
Germany Cropland 82.1 8.5 1.3 3742 328 40.6 1
Germany Cropland 59.5 7.2 1.0 3408 283 40.3 1
Germany Cropland 136.2 14.4 1.7 4658 365 50.3 1
Germany Cropland 306.7 32.8 3.9
1014
2 789 120.3 1
Germany Cropland 69.2 9.7 1.0 8900 694 88.4 1
Germany Cropland 47.3 5.8 0.7 7542 621 127.4 1
Germany Cropland 52.6 8.5 1.1 7717 619 143.9 1
Germany Cropland 22.6 2.9 0.3 1408 119 28.4 1
Germany Cropland 16.3 1.7 0.2 1875 126 47.4 1
Germany Cropland 43.9 5.5 0.8 3508 262 71.6 1
Germany Cropland 26.3 3.4 0.5 2908 191 59.7 1
Germany Cropland 38.8 4.9 0.6 2958 226 61.6 1
Germany Cropland 23.7 2.9 0.3 2800 214 66.8 1
Germany Cropland 29.1 3.6 0.4 3783 260 71.9 1
Germany Cropland 35.4 4.6 0.5 3200 242 68.4 1
India Grassland 26.0 2.30 0.612 1499 71.4 3.23 3
India
Tropical/Subtropical
Forest 28.9 3.1 0.6 2042 124 10.6 1
23
IndiaTropical/Subtropical
Forest 21.3 2.2 0.4 950 54.3 7.7 1
India
Tropical/Subtropical
Forest 52.2 5.19 0.90 2248 71.4 9.69 3
India
Tropical/Subtropical
Forest 34.9 4.30 0.70 1499 50.0 9.69 3
Germany
Temperate Broadleaf
Forest 22.3 2.6 0.6 1517 71.4 5.8 1
Germany
Temperate Broadleaf
Forest 43.2 4.1 1.9 2267 136 25.7 1
Germany
Temperate Broadleaf
Forest 45.4 8.7 1.1 2592 164 9.3 1
Germany
Temperate Broadleaf
Forest 39.0 5.7 1.0 2642 150 8.1 1
Germany
Temperate Broadleaf
Forest 53.8 5.9 1.5 3117 179 10.7 1
Germany
Temperate Broadleaf
Forest 73.1 7.8 1.6 3225 207 12.8 1
Germany
Temperate Broadleaf
Forest 73.0 9.1 2.2 3308 221 22.2 1
Germany
Temperate Broadleaf
Forest 60.9 8.9 1.3 3733 264 16.5 1
Germany
Temperate Broadleaf
Forest 67.2 7.6 1.2 3917 243 12.0 1
Germany
Temperate Broadleaf
Forest 31.3 2.1 1.3 4050 179 8.6 1
Germany
Temperate Broadleaf
Forest 71.9 10.4 1.5 4208 250 15.4 1
Germany
Temperate Broadleaf
Forest 98.8 11.8 2.5 4283 307 17.8 1
Germany
Temperate Broadleaf
Forest 83.3 10.3 1.7 4342 229 15.6 1
Germany
Temperate Broadleaf
Forest 83.9 9.6 1.8 4383 229 14.1 1
Germany
Temperate Broadleaf
Forest 95.6 13.6 1.6 4583 293 14.2 1
Germany
Temperate Broadleaf
Forest 26.4 3.2 1.5 4642 179 10.8 1
Germany
Temperate Broadleaf
Forest 42.7 4.0 3.2 4750 171 9.8 1
Germany
Temperate Broadleaf
Forest 80.8 9.7 1.4 5050 279 11.5 1
Germany
Temperate Broadleaf
Forest 60.3 5.1 2.6 5300 229 10.4 1
Germany
Temperate Broadleaf
Forest 104.5 13.6 1.5 5333 364 19.8 1
Germany
Temperate Broadleaf
Forest 35.8 5.1 1.4 5408 329 22.5 1
Germany
Temperate Broadleaf
Forest 102.0 13.8 1.9 5425 329 19.7 1
Germany
Temperate Broadleaf
Forest 51.5 4.6 2.4 5675 379 57.2 1
Germany
Temperate Broadleaf
Forest 123.3 16.6 2.4 6125 414 16.0 1
Germany
Temperate Broadleaf
Forest 112.3 15.8 2.0 6258 364 14.6 1
24
GermanyTemperate Broadleaf
Forest 140.7 14.8 3.4 6942 457 22.5 1
Germany
Temperate Broadleaf
Forest 118.1 15.9 2.6 7000 457 20.4 1
Germany
Temperate Broadleaf
Forest 56.5 8.8 1.7 7275 450 26.6 1
Germany
Temperate Broadleaf
Forest 33.7 3.1 1.1 7317 236 6.7 1
Germany
Temperate Broadleaf
Forest 57.6 4.7 3.6 7408 243 8.7 1
Germany
Temperate Broadleaf
Forest 168.3 16.4 3.1 8050 529 21.4 1
Germany
Temperate Broadleaf
Forest 80.8 11.4 1.2 8292 407 18.6 1
Germany
Temperate Broadleaf
Forest 89.0 8.4 2.7 8492 486 22.5 1
Germany
Temperate Broadleaf
Forest 58.7 2.9 3.1 9083 336 9.6 1
Germany
Temperate Broadleaf
Forest 87.4 12.3 3.8 9517 393 18.3 1
Germany
Temperate Broadleaf
Forest 176 24.8 3.7 9683 636 24.5 1
Germany
Temperate Broadleaf
Forest 103 6.9 5.6
1133
3 521 17.0 1
Germany
Temperate Broadleaf
Forest 92 4.8 3.5
1504
2 779 11.9 1
United States
of America Tundra 125 17.1 1.5
1958 3
117
9 38.5 1
United States
of America Tundra 233 29.3 5.4
2175 0
132
1 46.3 1
United States
of America Tundra 142 15.0 1.0
1558
3 964 31.7 1
United States
of America Tundra 233 34.3 2.3
1958 3
117
9 38.5 1
United States
of America Tundra 292 66.4 5.0
2175 0
132
1 46.3 1
United States
of America Tundra 125 15.0 1.1
1558
3 964 31.7 1
United States
of America Tundra 117 13.6 1.1
1958 3
117
9 38.5 1
United States
of America Tundra 125 19.3 2.0
2175 0
132
1 46.3 1
United States
of America Tundra 108 9.3 0.8
1558
3 964 31.7 1
China Cropland 13.8 4.2 0.1 1275 121 18.7 1
China Cropland 3.9 1.9 0.0 1275 121 18.7 1
China Cropland 10.2 0.8 0.1 1275 121 18.7 1
United
Kingdom Grassland 34.3 4.1 1.0 3250 214 25.8 1
United
Kingdom Grassland 43.4 7.5 1.3 2583 214 32.3 1
United
Kingdom Grassland 58.2 9.2 2.2 3917 357 41.9 1
United
Kingdom Grassland 60.3 8.5 2.6 2583 214 25.8 1
25
UnitedKingdom Grassland 68.2 9.9 2.5 2750 286 29.0 1
United
Kingdom Grassland 71.0 4.3 2.1 4167 357 19.4 1
United
Kingdom Grassland 75.0 6.2 2.2 3333 214 12.9 1
United
Kingdom Grassland 76.9 8.4 2.5 2500 214 16.1 1
United
Kingdom Grassland 78.4 9.5 3.1 2417 286 32.3 1
United
Kingdom Grassland 80.4 11.4 1.8 4667 429 32.3 1
United
Kingdom Grassland 88.2 11.4 2.6 3667 357 32.3 1
United
Kingdom Grassland 93.0 13.6 2.2 5000 357 35.5 1
United
Kingdom Grassland 102 14.0 3.1 3333 286 19.4 1
United
Kingdom Grassland 103 10.9 2.5 3750 286 41.9 1
United
Kingdom Grassland 104 14.9 2.4 4000 357 29.0 1
United
Kingdom Grassland 104 12.6 3.6 3833 357 35.5 1
United
Kingdom Grassland 108 11.6 4.7 4000 357 25.8 1
United
Kingdom Grassland 119 17.5 3.8 4917 429 48.4 1
United
Kingdom Grassland 119 7.6 3.3 3750 286 19.4 1
United
Kingdom Grassland 129 12.9 3.8 3500 286 25.8 1
United
Kingdom Grassland 133 12.1 3.9 3083 286 19.4 1
United
Kingdom Grassland 148 18.9 4.3 5667 571 32.3 1
United
Kingdom Grassland 152 18.1 3.5 3917 357 29.0 1
United
Kingdom Grassland 159 15.6 4.5 4000 357 32.3 1
United
Kingdom Grassland 170 17.5 5.3 5750 500 32.3 1
United
Kingdom Grassland 173 16.4 4.1 3667 357 32.3 1
United
Kingdom Grassland 185 17.4 5.1 5500 500 29.0 1
United
Kingdom Grassland 186 16.2 4.0 5333 500 25.8 1
United
Kingdom Grassland 284 24.7 7.7 6667 643 64.5 1
India Cropland 18.3 2.1 0.39 1000 107 15.2 1
India Cropland 24.3 2.4 0.52 1083 146 22.6 1
India
Tropical/Subtropical
Forest 63.7 5.1 1.0 1250 170 21.0 1
India Cropland 22.8 2.0 0.2 542 42.9 5.2 1
26
India Cropland 17.3 1.8 0.2 458 42.9 3.9 1
India Cropland 14.3 2.8 0.4 542 42.9 5.2 1
India Cropland 12.8 2.5 0.3 458 42.9 3.9 1
India Savanna 22.8 2.6 0.3 1333 78.6 3.5 1
India Savanna 21.7 3.6 0.5 1333 78.6 3.5 1
India
Tropical/Subtropical
Forest 33.4 3.7 0.5 1500 92.9 4.5 1
India
Tropical/Subtropical
Forest 41.2 4.7 0.7 2483 136 3.5 1
India
Tropical/Subtropical
Forest 29.7 3.1 0.4 1842 92.9 4.2 1
India
Tropical/Subtropical
Forest 19.8 1.7 0.2 408 21.4 4.2 1
India
Tropical/Subtropical
Forest 31.3 4.7 0.6 1500 92.9 4.5 1
India
Tropical/Subtropical
Forest 31.8 5.2 0.7 2483 136 3.5 1
India
Tropical/Subtropical
Forest 20.7 4.2 0.5 1842 92.9 4.2 1
India
Tropical/Subtropical
Forest 10.6 2.0 0.3 408 21.4 4.2 1
Nigeria Pasture 32.8 2.9 0.7 1092 63.3 6.3 1
Nigeria Pasture 29.9 3.4 2.0 1992 119 7.6 1
Nigeria Pasture 24.9 4.4 0.8 1083 61.1 6.2 1
Nigeria Pasture 43.2 5.1 3.5 1100 78.0 5.5 1
Nigeria Pasture 32.8 8.5 3.6 1217 85.1 7.3 1
India Cropland 20.8 2.4 0.5 888 76.1 5.6 1
India Cropland 20.8 2.4 0.5 887 76.0 5.7 4
India Savanna 38.8 3.1 0.7 792 92.9 6.5 1
India Savanna 38.0 3.1 0.7 700 85.7 6.5 1
India Savanna 32.9 2.5 0.5 833 78.6 5.5 1
India Savanna 35.6 2.9 0.6 967 73.6 7.7 1
India Savanna 30.1 2.6 0.5 1000 77.9 8.1 1
India Savanna 33.1 2.7 0.6 1004 77.0 7.0 1
India Savanna 33.1 2.7 0.6 1003 77.0 7.0 4
India
Tropical/Subtropical
Forest 50.8 4.6 0.8 1817 161 11.6 1
India
Tropical/Subtropical
Forest 50.7 4.6 0.8 1815 161 11.6 4
India
Tropical/Subtropical
Forest 69.3 5.9 1.2 3042 329 24.5 1
India
Tropical/Subtropical
Forest 44.5 3.4 0.9 2892 293 18.1 1
India Cropland 17.6 1.4 0.2 600 50.0 3.2 1
India Cropland 20.5 1.8 0.3 683 57.1 4.0 1
India Cropland 26.1 1.9 0.7 933 64.3 4.6 1
India Cropland 31.4 2.4 0.6 950 71.4 5.6 1
India Cropland 20.3 1.8 0.3 642 71.4 5.7 1
India Cropland 23.3 2.1 0.4 575 57.1 6.3 1
27
India Cropland 27.4 2.6 0.4 775 78.6 5.8 1
India Cropland 29.6 3.0 0.5 642 78.6 6.1 1
India Cropland 17.6 2.0 0.3 600 45.0 3.8 1
India Cropland 23.7 2.9 0.5 617 53.4 6.5 1
India Cropland 30.3 2.9 0.6 750 59.6 5.6 1
India Cropland 26.2 3.0 0.6 625 51.0 4.0 1
India Savanna 28.1 2.3 0.7 1332 71.4 6.46 3
India
Tropical/Subtropical
Forest 54.4 5.40 1.00 2331 143 9.69 3
India
Tropical/Subtropical
Forest 45.2 5.00 0.84 1832 71.4 9.69 3
China Cropland 29.3 2.7 0.6 1773 92.3 7.9 1
China Cropland 7.7 1.4 0.1 1308 164 36.5 1
China Cropland 4.6 1.8 0.0 1308 164 36.5 1
China Cropland 5.8 1.3 0.1 1308 164 36.5 1
China Grassland 37.0 3.9 0.7 2067 109 10.5 1
China
Tropical/Subtropical
Forest 50.8 6.1 1.0 1756 163 16.3 1
New Zealand Pasture 53.1 11.4 2.5 4333 214 16.8 1
New Zealand Pasture 76.3 21.5 3.2 5667 357 28.4 1
New Zealand Shrub 52.6 7.1 1.5 6000 329 12.3 1
New Zealand
Temperate Coniferous
Forest 40.7 5.2 1.0 4500 186 9.4 1
New Zealand
Temperate Coniferous
Forest 48.79 4.49 1.69 5000 150 14.19 1
China Cropland 22.0 5.3 0.1 1483 136 34.8 1
China Cropland 15.9 3.3 0.1 1483 136 34.8 1
China Cropland 16.1 3.9 0.1 1483 136 34.8 1
China
Tropical/Subtropical
Forest 21.2 1.5 0.5 950 80.7 5.1 1
China
Tropical/Subtropical
Forest 24.3 2.7 0.5 975 95.7 5.5 1
China
Tropical/Subtropical
Forest 22.7 2.4 0.6 1025 93.6 5.8 1
Czech Republic Cropland 39.0 3.5 2.3 3100 200 21.0 1
Czech Republic Cropland 38.0 3.2 1.1 3000 170 35.0 1
Czech Republic Grassland 66.8 7.1 3.8 5900 340 25.0 1
Czech Republic Grassland 156 19.0 7.8 9500 550 31.0 1
China Paddy field 25.14 2.49 0.23 1938 151 27.3 2
China Paddy field 34.25 3.66 0.26 1603 146 29.2 2
China Paddy field 29.42 2.94 0.36 1657 172 27 2
China Paddy field 42.37 4.57 0.39 1663 158 31.5 2
China Paddy field 26.41 3.44 0.40 1801 151 28.3 2
China Paddy field 42.42 3.85 0.43 1687 156 35 2
China Paddy field 58.66 5.10 0.44 2318 221 28.4 2
China Paddy field 26.79 4.37 0.46 1860 141 28.5 2
China Paddy field 56.10 6.66 0.48 2308 217 35 2
China Paddy field 38.11 4.40 0.49 1606 150 40 2
28
China Paddy field 33.09 4.93 0.50 1850 147 32 2
China Paddy field 63.45 6.86 0.54 2192 213 33.9 2
China Paddy field 70.77 6.51 0.56 2299 213 30.8 2
China Paddy field 43.87 6.48 0.58 1511 119 31.3 2
China Paddy field 65.78 5.63 0.59 2306 216 35.2 2
China Paddy field 30.03 5.20 0.61 1641 174 23.6 2
China Paddy field 18.49 2.82 0.63 1201 123 40.6 2
China Paddy field 30.28 6.94 0.66 1811 137 24 2
China Paddy field 42.50 6.29 0.66 1682 162 28.3 2
China Paddy field 77.75 7.83 0.67 3167 237 33.5 2
China Upland 28.63 3.21 0.18 1283 126 36.6 2
China Upland 39.07 3.10 0.24 1594 155 35.6 2
China Upland 33.85 3.36 0.24 1205 126 20.1 2
China Upland 25.05 2.64 0.25 1266 118 26 2
China Upland 20.83 3.85 0.25 1100 99.3 28.4 2
China Upland 26.72 2.99 0.30 969 98.6 34.8 2
China Upland 38.41 5.46 0.32 1468 146 28.7 2
China Upland 43.32 3.40 0.33 2065 181 37.5 2
China Upland 20.32 3.53 0.38 1009 102 25.3 2
China Upland 40.03 4.20 0.38 1641 143 36 2
China Upland 24.28 3.38 0.41 1174 106 40.7 2
China Upland 41.11 5.30 0.41 1480 141 26.4 2
China Upland 26.47 4.17 0.43 1491 127 36.4 2
China Upland 32.03 4.59 0.46 1626 139 40.6 2
China Upland 32.72 3.87 0.48 1200 125 31.4 2
China Upland 34.58 5.65 0.49 1419 136 33.3 2
China Upland 53.90 4.43 0.51 2246 219 24.8 2
China Upland 33.90 4.44 0.53 1323 126 27 2
China Upland 38.88 4.98 0.54 1507 141 34.6 2
China Upland 50.39 5.96 0.56 1498 161 30.6 2
China Cropland 72.2 5.0 1.0 1375 143 14.7 1
China Cropland 57.9 4.2 1.1 1200 126 14.7 1
China Paddy field 19.73 2.27 0.46 917 96.3 12.4 2
China Paddy field 84.71 8.42 1.67 1485 157 17.3 2
China Paddy field 74.35 4.58 0.62 1650 164 17 2
China Paddy field 87.78 4.72 1.31 1630 161 13.7 2
China Paddy field 76.52 5.53 0.74 1271 135 17.9 2
China Paddy field 80.98 5.11 0.77 1591 167 16.2 2
China Paddy field 73.82 6.20 1.77 1411 145 24.4 2
China Paddy field 46.89 2.45 0.55 977 104 14.4 2
China Paddy field 68.25 3.62 0.58 1481 150 16.2 2
China Paddy field 77.01 4.83 0.69 1613 165 16.8 2
China Paddy field 48.37 2.20 0.74 1118 119 14.9 2
China Paddy field 52.09 2.26 0.46 1035 114 14.5 2
China Paddy field 62.89 3.03 0.62 1613 160 16.1 2
29
China Paddy field 52.57 2.88 0.81 1534 151 14.3 2
China Paddy field 57.38 3.34 0.54 1612 164 16.8 2
China Paddy field 69.95 3.16 1.31 1340 137 15.4 2
China Paddy field 50.00 3.01 1.13 1234 131 14 2
China Paddy field 59.78 3.86 0.93 1161 123 13.2 2
China Paddy field 55.78 3.11 0.77 1280 135 15.5 2
China Paddy field 56.27 3.95 0.66 1095 123 15.6 2
China Upland 10.85 1.46 0.76 817 87 18.7 2
China Upland 72.92 5.56 0.63 1478 152 14.6 2
China Upland 38.79 2.60 0.53 1055 106 11.8 2
China Upland 25.98 1.47 0.62 923 98.5 14 2
China Upland 11.41 0.68 0.13 672 64.7 9.3 2
China Upland 42.18 2.57 0.64 1113 116 13.1 2
China Upland 12.74 0.98 0.83 1082 109 17.8 2
China Upland 33.62 1.76 0.50 961 102 12.4 2
China Upland 12.41 1.22 0.16 682 67.8 12.4 2
China Upland 27.06 1.39 0.62 944 97.8 14.7 2
China Upland 38.82 1.61 0.58 942 103 14.1 2
China Upland 44.81 2.80 0.89 1242 129 15.6 2
China Upland 39.08 3.39 0.64 966 102 17 2
China Upland 20.59 2.67 0.55 855 82 15.7 2
China Upland 18.31 1.99 0.17 692 68.2 13.4 2
China Upland 14.00 1.86 0.17 703 71 13.8 2
China Upland 58.22 6.56 0.70 1006 96.2 14.2 2
China Upland 12.07 0.81 0.42 762 78.6 15.6 2
China Upland 15.39 1.63 0.41 779 79.1 18.4 2
China Upland 34.30 2.87 0.43 1181 123 16.2 2
China Woodland 38.07 2.75 0.30 1085 116 17.4 2
China Woodland 14.60 1.58 0.39 833 90.9 12.6 2
China Woodland 13.75 1.30 0.95 813 91 18.9 2
China Woodland 11.65 1.67 0.24 682 71.7 10.8 2
China Woodland 14.39 1.40 0.41 837 86.6 20.4 2
China Woodland 14.59 1.19 0.71 967 94.3 27 2
China Woodland 14.61 1.74 0.37 1038 101 15.8 2
China Woodland 15.35 1.44 0.46 988 97.9 19.1 2
China Woodland 9.45 1.00 0.12 1017 94.8 13.3 2
China Woodland 13.36 1.35 0.38 839 85.6 14.5 2
China Woodland 16.23 1.49 0.15 1053 107 15.3 2
China Woodland 15.13 1.14 0.40 993 107 25.7 2
China Woodland 11.74 0.67 0.35 1045 97.1 26.2 2
China Woodland 13.41 1.03 0.21 870 91.8 14.8 2
China Woodland 15.74 1.58 0.74 823 90.7 18.2 2
China Woodland 13.62 1.37 0.40 856 93.2 13.3 2
China Woodland 12.05 1.38 0.35 847 86.3 15.8 2
China Woodland 12.30 1.02 0.21 747 75.6 13.1 2
30
China Woodland 10.25 0.81 0.39 835 86.6 18.8 2
China Woodland 14.16 2.07 0.43 836 85.3 15.8 2
China Woodland 16.44 1.95 0.30 875 90 11.4 2
China Woodland 11.84 2.26 0.17 750 76.4 11 2
China Woodland 8.27 1.33 0.21 752 78.6 16.3 2
China Woodland 14.50 2.09 0.24 968 94.3 10.4 2
China Woodland 16.55 2.38 0.25 819 82.7 10.5 2
China Woodland 8.80 1.28 0.19 666 66.6 9.5 2
China Woodland 10.17 1.32 0.40 664 70.3 18.6 2
China Woodland 22.00 2.06 0.28 786 80.6 12.5 2
China Woodland 13.05 1.76 0.25 885 92.1 13.4 2
China Woodland 14.29 2.29 0.65 828 89.2 17 2
China Paddy field 160.5
7 7.16 1.30
1517 154 11.8 2
China Paddy field 57.77 4.43 0.71 1174 109 12 2
China Paddy field 24.54 1.94 0.27 812 78.2 13 2
China Paddy field 123.2
9 5.11 0.55
1290 121 10.1 2
China Paddy field 75.43 3.57 0.51 1294 115 9.5 2
China Paddy field 85.72 4.00 0.48 1459 138 12.4 2
China Paddy field 48.04 4.41 0.69 1224 110 14.5 2
China Paddy field 26.65 1.98 0.30 791 72.6 9.6 2
China Paddy field 26.71 1.94 0.30 934 86.9 12.3 2
China Paddy field 164.0
3 8.74 1.21
2094 195 17.4 2
China Paddy field 274.0
0
12.2
4 2.43
2774 232 17.6 2
China Paddy field 307.4
7
14.7
7 2.87
3055 274 23.9 2
China Paddy field 341.2
2
17.1
6 1.92
3737 314 19.1 2
China Paddy field 201.1
7
10.2
4 1.20
2504 213 15 2
China Paddy field 256.9
6
12.2
2 2.07
2881 252 17.3 2
China Paddy field 322.7
7
17.0
1 1.79
3689 305 24.3 2
China Paddy field 365.4
9
14.6
6 2.67
3173 282 19 2
China Paddy field 351.3
2
13.6
4 2.33
2925 258 18 2
China Paddy field 83.03 5.62 0.53 1782 153 19.2 2
China Paddy field 115.5
5 6.99 1.18
2016 188 23.5 2
China Upland 53.45 5.71 0.64 1639 170 39.7 2
China Upland 108.1
0
11.0
2 1.61
1685 179 25.2 2
China Upland 39.45 3.83 0.31 1409 135 24.1 2
China Upland 25.48 2.45 0.38 1323 143 24.6 2
China Upland 23.45 2.48 0.36 982 104 24.6 2
31
China Upland 16.99 2.63 0.41 933 109 26.7 2
China Upland 15.53 1.45 0.12 844 114 24.2 2
China Upland 16.70 1.13 0.47 995 133 28.3 2
China Upland 6.70 0.61 0.12 605 73.3 21.3 2
China Upland 15.22 1.26 0.27 555 77.2 13.1 2
China Upland 7.69 1.47 0.22 568 73.3 12.8 2
China Upland 57.30 3.45 0.60 1119 141 16.7 2
China Upland 8.96 2.35 0.83 567 76 20.9 2
China Upland 63.91 4.77 0.94 1531 169 18.4 2
China Upland 11.21 2.00 0.69 747 95.8 14.8 2
China Upland 8.69 0.75 0.11 576 77.9 11.6 2
China Upland 14.77 1.19 0.22 717 94.9 13.4 2
China Upland 18.38 2.05 1.01 802 100 14.1 2
China Upland 14.57 2.91 0.99 1016 108 27.3 2
China Upland 9.42 1.20 0.11 1003 111 26.7 2
China Woodland 70.41 8.00 0.81 2635 227 41.2 2
China Woodland 64.48 7.84 0.61 2196 179 23 2
China Woodland 86.45 11.1
6 0.95
3061 269 45.3 2
China Woodland 45.34 7.05 0.67 1966 167 31.8 2
China Woodland 10.94 1.09 0.30 771 58.8 8.9 2
China Woodland 14.21 1.56 0.31 770 61.4 8.5 2
China Woodland 14.79 3.65 0.18 824 65.7 8.6 2
China Woodland 12.73 2.20 0.77 956 74 9.6 2
China Woodland 15.44 2.55 0.42 814 58.3 6.8 2
China Woodland 22.77 4.31 0.60 1055 77 6.4 2
China Woodland 41.73 4.88 0.37 1291 102 9.2 2
China Woodland 43.68 6.37 0.62 1330 110 8.2 2
China Woodland 30.95 6.13 0.67 1398 106 9.8 2
China Woodland 16.24 2.21 0.13 870 76.5 6.9 2
China Woodland 28.01 3.72 0.51 1532 117 7.8 2
China Woodland 15.83 2.56 0.16 901 79.6 6.1 2
China Woodland 14.13 1.91 0.30 862 58.9 6 2
China Woodland 26.61 3.49 0.32 984 85.2 6.6 2
China Woodland 12.88 2.78 0.26 1142 84.6 6.7 2
China Woodland 57.52 5.43 0.72 1679 124 8.6 2
China Woodland 15.67 1.65 0.25 801 71.4 6.4 2
China Woodland 10.82 1.57 0.27 769 74.3 6.7 2
China Woodland 53.50 9.26 0.49 2754 230 20.8 2
China Woodland 40.03 4.78 0.55 2624 213 22.9 2
China Woodland 80.33 10.3
1 0.72
1856 161 20.4 2
China Woodland 57.80 8.85 0.54 1782 155 21.1 2
China Woodland 51.51 8.04 0.92 2170 184 19.8 2
China Woodland 62.03 9.80 0.85 1965 171 24 2
32
China Woodland 61.48 11.2
6 0.85
2184 187 21.4 2
China Woodland 58.03 8.78 0.58 1768 151 22.7 2
India Cropland 26.1 2.4 0.5 1078 93.1 6.6 1
India Grassland 34.7 3.0 0.7 1240 107 7.8 1
India
Tropical/Subtropical
Forest 53.7 5.9 0.9 1680 144 11.7 1
China Cropland 16.8 2.3 0.1 1442 163 51.0 1
China Cropland 17.9 2.2 0.2 1442 163 51.0 1
China Cropland 19.8 2.7 0.3 1442 163 51.0 1
New Zealand Pasture 133 14.1 4.7 9167 493 44.8 1
New Zealand
Tropical/Subtropical
Forest 133 17.3 3.1 9667 400 9.3 1
New Zealand
Tropical/Subtropical
Forest 71.5 7.0 1.6 6833 293 32.2 1
India Cropland 39.3 3.4 0.58 1167 136 22.6
China Cropland 45.3 5.1 0.3 1667 184 48.4 1
China Cropland 27.9 3.6 0.2 1667 184 48.4 1
China Cropland 27.3 5.1 0.4 1667 184 48.4 1
India
Tropical/Subtropical
Forest 27.8 4.1 0.6 3000 286 12.6 1
India
Tropical/Subtropical
Forest 56.7 6.3 0.8 4500 357 16.8 1
India
Tropical/Subtropical
Forest 90.6 8.8 1.4 5167 429 19.2 1
Australia Cropland 22.8 2.8 0.2 1033 72.9 11.1 1
Australia Cropland 26.3 3.4 0.3 1117 79.3 11.4 1
Australia Cropland 26.1 2.6 0.2 1033 79.3 10.9 1
Australia Cropland 28.9 3.3 0.3 1150 82.1 11.8 1
New Zealand Pasture 118 17.5 3.8 4400 316 20.6 1
New Zealand Pasture 90.6 16.4 4.5 4575 343 26.5 1
Colombia Cropland 6.0 1.3 0.1 2058 119 11.4 1
Colombia Pasture 14.4 2.5 0.2 2367 129 8.8 1
Colombia Savanna 12.1 1.9 0.2 2175 120 7.0 1
India Cropland 21.6 2.4 0.42 1417 143 14.8 1
India Cropland 32.5 2.7 0.52 1833 143 24.2 1
India Cropland 48.6 4.5 0.71 1833 164 24.5 1
India
Tropical/Subtropical
Forest 59.3 6.9 1.0 2167 176 21.3 1
India
Tropical/Subtropical
Forest 67.5 5.3 1.2 1317 471 100.0 1
India
Tropical/Subtropical
Forest 47.9 3.1 0.9 883 300 58.1 1
India
Tropical/Subtropical
Forest 30.7 2.2 0.6 767 186 48.4 1
India
Tropical/Subtropical
Forest 64.1 5.2 1.3 1393 474 29.1 1
India
Tropical/Subtropical
Forest 36.2 2.6 0.7 794 270 23.6 1
India Tropical/Subtropical 29.8 2.4 0.7 643 182 16.1 1
