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Climatic suitability of the distribution of the winter wheat cultivation zone in China
Europ. J. Agronomy 43 (2012) 77–86
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Climatic suitability of the distribution of the winter wheat cultivation zone in China Sun Jing-Song a,c , Zhou Guang-Sheng b,a,∗ , Sui Xing-Hua a a b c
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, No. 20 Nanxincun, Xiangshan, Beijing 100093, China Chinese Academy of Meteorological Sciences, Beijing 100081, China Graduate University of Chinese Academy of Sciences, Beijing 100049, China
a r t i c l e
i n f o
Article history: Received 13 October 2011 Received in revised form 20 May 2012 Accepted 21 May 2012 Keywords: China Winter wheat Cultivation zone Climate suitability Distribution
a b s t r a c t Winter wheat is one of major grain crops in China. To scientifically map cropping patterns, it is very important to understand the area of its viable cultivation zone in China. Based on published data, geographical information, national climate data, and the MaxEnt model, the relationship between the distribution of the winter wheat cultivation zone and climate was established. The main indices controlling the distribution of the winter wheat cultivation zone were analyzed to reveal climatic suitability. The main controls on winter wheat distribution in China were: the negative accumulation of daily mean temperatures below 0 ◦ C during winter (i.e., negative accumulated temperature), annual mean extreme minimum temperatures, potential evapotranspiration, and annual precipitation. For winter wheat to safely survive the winter, the negative accumulated temperature should be higher than −700 ◦ C, and the annual mean extreme minimum temperature should be higher than −30 ◦ C. Climate suitability classification of the winter wheat cultivation zone was mapped, based on the MaxEnt probability distribution of winter wheat. Former studies indicated that the northeast boundary of the winter wheat cultivation zone is the south of Liaoning Province, and our study indicated that the northeast boundary of the winter wheat cultivation zone is the north of Heilongjiang Province; former studies indicated that the northwest boundary of the winter wheat cultivation zone is the south of Xinjiang Uygur Autonomous Region, and our study indicated that the northwest boundary of the winter wheat cultivation zone is the north of Xinjiang Uygur Autonomous Region. Our study describes a suitable winter wheat cultivation zone in China and the northern boundary of winter wheat cultivation, and it will be helpful for guiding the cultivation of Chinese winter wheat. Based on our climatic indices and their threshold determining the distribution of winter wheat, it will be helpful for gaining a scientific understanding of the effects of climate change. © 2012 Elsevier B.V. All rights reserved.
1. Introduction China is the largest wheat producer and consumer in the world (Wang et al., 2004). Wheat ranks as the third leading crop in China after rice and maize (Wang et al., 2009). It is thus important to determine the distribution of the winter wheat cultivation zone in order to help guarantee high and stable yields. Wheat is widely distributed from the Arctic to the equator and from lowlands to highlands (Curtis, 2002). However, different wheat cultivars have different heat requirements. Two cultivars predominate: spring wheat and winter wheat, based on seasonal growth and development characteristics. Winter wheat is generally planted in
∗ Corresponding author at: State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, No. 20 Nanxincun, Xiangshan, Beijing 100093, China. Tel.: +86 10 62836268; fax: +86 10 82595962. E-mail address: [email protected] (Z. Guang-Sheng). 1161-0301/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.eja.2012.05.009
September and October (William et al., 2010). Winter wheat may gain higher returns than spring wheat, for four reasons: (1) planting winter wheat can increase the multiple crop index; (2) the quality of winter wheat is better than spring wheat in terms of concentrations of protein and wet gluten; (3) planting winter wheat can gain higher yields due to its longer growing season; (4) planting winter wheat need less seed quantities and can improve yields at low costs (Zhou et al., 2001). Winter wheat is very popular in China because of its high yield, and accounts for around 85% of production and acreage (He et al., 2001). The distribution of the winter wheat cultivation zone in China has interested growing numbers of scientists, and a wealth of literature has been published (Zheng and Newman, 1986; Yang et al., 2010). These studies have determined the north boundary of the cultivation zone according to overwintering heat requirements of winter wheat. The climatic indices of the winter wheat cultivation zone in China have all been based on a bearable low temperature for winter wheat, such as an annual mean extreme minimum
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Table 1 Potential climatic indices affecting the distribution of winter wheat cultivation zone in China. Potential climatic indices Humid index Annual precipitation Penman aridity index Thornthwaite aridity index Holdridge aridity index Growing seasons length or heat required Potential evapotranspiration of Penman model Potential evapotranspiration of Thornthwaite model Potential evapotranspiration of Holdridge Life’s Zones Annual average temperature ≥0 ◦ C interval days ≥10 ◦ C interval days ≥0 ◦ C accumulated temperature ≥10 ◦ C accumulated temperature Biotemperature Winter index Annual extremely low temperature Lowest monthly average temperature negative accumulation of daily mean temperature < 0 ◦ C during the winter Vernalization index Lowest monthly average temperature Duration in days of 0–3 ◦ C Duration in days of 0–7 ◦ C
Abbreviation
Refs.
Annual precipitation Penman AI Thornthwaite AI Holdridge AI
1–7,11–14 7,8 7,8 7
Penman PET Thornthwaite PET Holdridge PET Annual average temperature ≥0 ◦ C interval days ≥10 ◦ C interval days ≥0 ◦ C accumulated temperature ≥10 ◦ C accumulated temperature Biotemperature
7,8 7,8 7,10 1,4,8 5 1 5 1 4,6,8,9,10
Annual extremely low temperature Lowest monthly average temperature Negative accumulated temperature
11,12 1,2,11,16 14,17
Lowest monthly average temperature 0–3 ◦ C vernalization days 0–7 ◦ C vernalization days
18 15 15
Refs. [1] Yan et al. (2011); [2] Prentice et al. (1992); [3] Köppen (1936); [4] Whittaker (1975); [5] Box (1981); [6] Holdridge (1947); [7] Gao and Giorgi (2008); [8] Mohammad et al. (2010); [9] Mather and Yoshioka (1968); [10] Harris (1973); [11] Yang et al. (2010); [12] Porter and Gawith (1999); [13] Zheng (1981); [14] Zheng and Newman (1986); [15] Deng (1993); [16] Yun et al. (2007); [17] Newman (1983); [18] William et al. (2010).
temperature, the lowest monthly average temperature, or the negative accumulation of daily mean temperature below 0 ◦ C during winter (i.e., negative accumulated temperature) (Newman, 1983; Porter and Gawith, 1999; Rosenzweig, 1985; Veisz et al., 1996; Yang et al., 2010, 2011; Zheng and Newman, 1986). Based on above overwintering indices mentioned, studies showed that the distribution of winter wheat has been limited in northern but not in southern China (Yun et al., 2007). According to the distribution mechanism of plant species, there may be three types of climatic indices used to determine winter wheat distribution: (1) the minimum temperature that a plant can tolerate; (2) the growing season length and heat requirements for completion of its life cycle; (3) the water supply necessary for plant canopy formation and maintenance (Woodward, 1987). Former studies have mainly focused on the first point, the minimum temperature that plant can tolerate (Newman, 1983; Porter and Gawith, 1999; Rosenzweig, 1985; Veisz et al., 1996; Yang et al., 2011; Zheng and Newman, 1986), fewer studies have paid attention to the length of the growing season (i.e., the heat required), or to the water supply. Under double cropping systems, where winter wheat and other crops occupy the whole growing season, the effects of growing season length and heat requirements should not be ignored. Meanwhile, the water supply for plant canopy formation and maintenance becomes the key factor determining the distribution of winter wheat in the arid and semi-arid areas of North China. Both areas with too much precipitation (more than the water requirement for winter wheat development) and too little precipitation (not enough for winter wheat development) will decrease winter wheat yield. Growth and developmental phases of wheat are controlled by vernalization (William et al., 2010), so the climatic indices of vernalization have not been ignored in the literature. The main short-comings of the former studies were: (1) former climate indices have been based on small areas and small experiments, and have lacked objectivity, quantification, and technical explanation (e.g., the distribution mechanism of plant species as mentioned above); (2) these climatic indices have not been tested at regional scales, so different researchers have designed different
climatic indices as they have focused on differing spatial and temporal scales; and (3) former studies have paid more attention to changes in the northern boundary of winter wheat planting, rather than conducting fine analyses at regional scales. We considered the published literature and the development of different winter wheat cultivars in different regions, and gathered the potential climatic indices affecting the distribution of the winter wheat at regional and annual scales. The MaxEnt model is a method to evaluate species distribution when presence-only data exist, and it was successfully applied for predicting species invasion, plant diseases and insect pests, species conservation and so on (Phillips et al., 2006; Peavey, 2010), and therefore, we revealed the major climatic indices of the distribution of winter wheat cultivation zone, using a MaxEnt model, and simulated the MaxEnt probability distribution of winter wheat in China. Our objectives were: (1) to reveal the major climatic indices of winter wheat distribution and obtain the MaxEnt probability distribution of winter wheat in China; (2) to evaluate the climatic suitability of winter wheat cultivation distribution in China; and (3) to determine the threshold values for the main climatic indices affecting winter wheat cultivation distribution in China. 2. Material and methods 2.1. Selection of climatic indices Because of diverse phenophases of winter wheat with different cultivation areas, cultivation habits, and varieties (Jamieson et al., 1998), it is difficult to explain climatic indices affecting the distribution of the winter wheat at shorter time scales. Based on this and our target of exploring the distribution of winter wheat at national level, the climatic indices should be selected at regional and annual scales. Considering mechanisms of plant species distribution may apply to winter wheat, the selection of indices was not only based on the characteristics of winter wheat growth, but also the mechanisms of plant species distribution. Based on the above principles about selecting climatic indices and the published data, the climatic indices affecting the distribution of the winter wheat cultivation
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Fig. 1. Distribution of winter wheat (A) and weather station (B) in China.
Table 2 Contributions of the climatic indices to the simulation results of the MaxEnt model. Variables abbreviations followed Table 1. Variable
Percent contribution
Permutation importance
Negative accumulated temperature Lowest monthly average temperature Penman PET Annual average temperature Annual precipitation Holdridge AI 0–3 ◦ C vernalization days ≥10 ◦ C interval days 0–7 ◦ C vernalization days Annual extremely low temperature Penman AI Biotemperature Thornthwaite AI ≥0 ◦ C accumulated temperature ≥10 ◦ C accumulated temperature ≥0 ◦ C interval days Thornthwaite PET Holdridge PET
26.1 19.2 10.4 9.6 6.9 6.7 6.7 3.6 2.7 2.1 1.6 1.4 1.2 0.6 0.6 0.3 0.1 0.1
41.3 0.3 11.2 0 14.3 0.4 4.3 2.2 1.7 14 4.7 0 0.4 0.7 3.3 0.1 0.8 0.2
zone can be classified to four types: (1) the minimum temperature that winter wheat can tolerate (winter index); (2) the growing season length or heat requirements for winter wheat to complete a life cycle; (3) water supply for winter wheat canopy formation and maintenance (humidity index); and (4) vernalization index of winter wheat (see details in Table 1). 2.2. Data processing Our data included geographic information about the distribution of winter wheat in China (1991–2007) (Fig. 1A), and daily climate data (1978–2007) from China’s ground climate data (Fig. 1B). These data were collected by National Meteorological Information Center, China Meteorological Administration. For fine grid analysis, the data containing daily temperature, precipitation, wind, and humidity were interpolated onto the surface grid data using a 10 km × 10 km resolution, following Thornton et al. (1997). We gained the surface data of solar radiation following the methods of Thornton and Running (1999). The aridity index (AI) (see details in Table 1, humid index) was calculated using AI = PET/P, where PET is the potential evapotranspiration, and P is the annual precipitation (Arora, 2002). There are three conventional calculations used to determine potential
evapotranspiration: the Penman model, the Thornthwaite model, and the Holdridge Life Zones system (Allen et al., 1998; Fang and Yoda, 1990). We used all three and compared results. We used the biotemperature used in the categorizing system of the Holdridge Life Zones (biotemperature is similar to the growing degree days, defined as the summation of monthly averaged temperature between 0 ◦ C and 30 ◦ C) (Holdridge, 1947). For eliminating the effects of random fluctuation of mean daily air temperature, immobile beginning and ending dates of ≥0 ◦ C and ≥10 ◦ C each year were determined with 5-day moving average method (Yan et al., 2011). 2.3. The MaxEnt model The MaxEnt model is a program for simulating species distribution when presence-only data exist (Phillips et al., 2006). Presence-only data, for which there is no information on locations where the species is absent, are common in both animal and plant studies. In many situations, these may be the only data available on a species. We need effective ways to use these data to explore species distribution or species use of habitat (Jennie and Mark, 2006). The MaxEnt model is able to predict the species’ distributions in broad unsampled regions and can explain results according
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Table 3 The Pearson correlation coefficient matrix between the climatic indices. ≥0 ◦ C accumulated temperature ◦
≥0 C accumulated temperature ≥0 ◦ C interval days ≥10 ◦ C accumulated temperature ≥10 ◦ C interval days 0–3 ◦ C vernalization temperature 0–7 ◦ C vernalization temperature Annual average temperature Annual extremely low temperature Annual precipitation Biotemperature Holdridge AI Holdridge PET Lowest monthly average temperature Negative accumulated temperature Penman AI Penman PET Thornthwaite AI Thornthwaite PET
≥0 ◦ C accumulated temperature ≥0 ◦ C interval days ≥10 ◦ C accumulated temperature ≥10 ◦ C interval days 0–3 ◦ C vernalization temperature 0–7 ◦ C vernalization temperature Annual average temperature Annual extremely low temperature Annual precipitation Biotemperature Holdridge AI Holdridge PET Lowest monthly average temperature Negative accumulated temperature Penman AI Penman PET Thornthwaite AI Thornthwaite PET
≥0 ◦ C interval days
1
**
.890 1 .866** .913** −.201** −.105** .953** .934** .786** .890** −.183** .890** .920** .938** −.504** .550** −.234** .897**
.890** .997** .946** −.539** −.452** .975** .883** .803** 1.000** −.091** 1.000** .864** .781** −.496** .417** −.156** .977**
≥10 ◦ C interval days .946** .913** .939** 1 −.515** −.411** .942** .865** .797** .946** −.132** .946** .842** .798** -.532** .410** −.199** .916**
.997 .866** 1 .939** −.572** −.511** .961** .855** .785** .997** −.075** .997** .835** .754** −.499** .384** −.143** .967**
0–3 ◦ C vernalization temperature
0–7 ◦ C vernalization temperature
Annual average temperature
Annual extremely low temperature
−.539** −.201** −.572** −.515** 1 .877** −.388** −.296** −.468** −.539** .089** −.539** −.279** −.040** .262** −.068** .130** −.485**
−.452** −.105** −.511** −.411** .877** 1 −.296** −.124** −.273** −.452** −.048** −.452** −.114** .015** .257** .096** .013** −.369**
.975** .953** .961** .942** −.388** −.296** 1 .943** .788** .975** −.078** .975** .929** .898** −.463** .512** −.133** .975**
.883** .934** .855** .865** −.296** −.124** .943** 1 .831** .883** −.209** .883** .993** .893** −.374** .726** −.228** .941**
Annual precipitation ◦
≥0 C accumulated temperature ≥0 ◦ C interval days ≥10 ◦ C accumulated temperature ≥10 ◦ C interval days 0–3 ◦ C vernalization temperature 0–7 ◦ C vernalization temperature Annual average temperature Annual extremely low temperature Annual precipitation Biotemperature Holdridge AI Holdridge PET Lowest monthly average temperature Negative accumulated temperature Penman AI Penman PET Thornthwaite AI Thornthwaite PET
≥0 ◦ C accumulated temperature ≥0 ◦ C interval days ≥10 ◦ C accumulated temperature ≥10 ◦ C interval days 0–3 ◦ C vernalization temperature 0–7 ◦ C vernalization temperature Annual average temperature Annual extremely low temperature Annual precipitation Biotemperature Holdridge AI
≥10 ◦ C accumulated temperature
**
Biotemperature
**
**
.803 .786** .785** .797** −.468** −.273** .788** .831** 1 .803** −.530** .803** .828** .633** −.587** .608** −.563** .845**
1.000 .890** .997** .946** −.539** −.452** .975** .883** .803** 1 −.091** 1.000** .864** .781** −.496** .417** −.156** .977**
Holdridge AI
Holdridge PET
−.091 −.183** −.075** −.132** .089** −.048** −.078** −.209** −.530** −.091** 1 −.091** −.217** −.053** .586** −.303** .987** −.150**
1.000** .890** .997** .946** −.539** −.452** .975** .883** .803** 1.000** −.091** 1 .864** .781** −.496** .417** −.156** .977**
**
Lowest monthly average temperature
Negative accumulated temperature
Penman AI
Penman PET
Thornthwaite AI
.864** .920** .835** .842** −.279** −.114** .929** .993** .828** .864** −.217**
.781** .938** .754** .798** −.040** .015** .898** .893** .633** .781** −.053**
−.496** −.504** −.499** −.532** .262** .257** −.463** −.374** −.587** −.496** .586**
.417** .550** .384** .410** −.068** .096** .512** .726** .608** .417** −.303**
−.156** −.234** −.143** −.199** .130** .013** −.133** −.228** −.563** −.156** .987**
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Table 3 ( continued )
Holdridge PET Lowest monthly average temperature Negative accumulated temperature Penman AI Penman PET Thornthwaite AI Thornthwaite PET **
Lowest monthly average temperature
Negative accumulated temperature
Penman AI
Penman PET
Thornthwaite AI
.864** 1 .891** −.347** .773** −.230** .935**
.781** .891** 1 −.386** .568** −.090** .814**
−.496** −.347** −.386** 1 −.003 .698** −.436**
.417** .773** .568** −.003 1 −.248** .576**
−.156** −.230** −.090** .698** −.248** 1 −.192**
P < 0.01.
to habitat (Peterson et al., 2007). Compared MaxEnt predictions with those of a commonly used presence-only modeling method, the Genetic Algorithm for Rule-Set Prediction (GARP), the area under the ROC curve (AUC) was almost always higher for MaxEnt, indicating better discrimination of suitable versus unsuitable areas for the species (Elith et al., 2011). Prediction by the MaxEnt model can be assessed using a receiver operating curve (ROC). A ROC plot is obtained by plotting all sensitivity values (true positive fraction) on the y axis against their equivalent (1– specificity) values (false positive fraction) for all available thresholds on the x axis. The area under the ROC function (AUC) is usually taken to be an important index because it provides a single measure of overall accuracy that is not dependent upon a particular threshold. The value of the AUC is between 0.5 and 1.0. The higher the value, the better the simulated results; and if the value was lower than 0.50, the simulated results were considered to make no sense (Fielding and Bell, 1997). If AUC > 0.78, the model performed well (Peavey, 2010). There are two methods to assess the contributions of climate indices to simulated models (Phillips and AT&T Research, 2011): (1) percentage contribution and permutation importance and (2) the jackknife test. While the MaxEnt model is being trained, it keeps track of which environmental variables are contributing to fitting the model. Each step of the MaxEnt algorithm increases the gain of the model by modifying the coefficient for a single feature; the program assigns the increase in the gain to the environmental variable
that the feature depends on. Converting to percentages at the end of the training process, we get the middle column in Table 2. These percent contribution values are only heuristically defined: they depend on the particular path that the MaxEnt code uses to get to the optimal solution, and a different algorithm could get to the same solution via a different path, resulting in different percent contribution values. In addition, when there are highly correlated environmental variables, the percent contributions should be interpreted with caution. The right-hand column in the table shows a second measure of variable contributions, called permutation importance. This measure depends only on the final MaxEnt model, not the path used to obtain it. The contribution for each variable is determined by randomly permuting the values of that variable among the training points (both presence and background) and measuring the resulting decrease in training AUC. A large decrease indicates that the model depends heavily on that variable. Values are normalized to give percentages. To get alternate estimates of variable importance, we can also run a jackknife test. When we run a jackknife test, a number of models are created. Each variable is excluded in turn, and a model created with the remaining variables. Then a model is created using each variable in isolation. In addition, a model is created using all variables, as before. The jackknife test is illustrated by a bar graph of black, hollow, and light gray bars (Fig. 2). The length of the hollow bar at the bottom represents the total score for simulating
Fig. 2. The bar chart of jackknife test.
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Fig. 3. Climate suitability of winter wheat cultivation zone in China.
the distribution of winter wheat using all the selected potential climatic indices. The length of the black bar represents the score using only one of the climatic indices; the longer the bar, the more important is the climatic factor. The length of the light gray bar represents the score of a model created with the remaining indices. The greater the difference in length between light gray and hollow bars, the less likely that the factor affecting winter wheat distribution can be replaced by other indices. The factor can only be replaced by other indices if the hollow and light gray bars show similar lengths. We used default parameters for the MaxEnt model (convergence threshold of 10−5 , the maximum iteration value of 1000 and automatic regularization with a value of 10−4 ), and the cumulative output format of the original results.
3. Results 3.1. Selection of the main climatic indices affecting winter wheat cultivation distribution Low annual extreme temperature can cause the death of winter wheat, and it is overwintering index of winter wheat. The annual extremely low temperature had a higher permutation importance, and the model depended heavily on this factor (Table 2). However, continuous temperature being higher than annual extreme temperature also leads to winter wheat death, so negative
accumulation or the lowest monthly average temperature is another overwintering index of winter wheat. A negative accumulated daily mean temperature of below 0 ◦ C during the winter, gained the highest values for percentage contribution and permutation importance (Table 2), and the longest black bar (in the jackknife test) (Fig. 2). At the same time, the permutation importance of the lowest monthly average temperature was very low (Table 2), the Pearson correlation coefficient matrix between the climatic indices showed that the lowest monthly average temperature was significantly strongly related to the negative accumulated temperature (r = 0.89, P < 0.0001), and the annual extremely low temperature (r = 0.99, P < 0.0001) (see details in Table 3). Based on this analysis, the information about the distribution of winter wheat provided by the lowest monthly average temperature could be replaced by annual extremely low temperature and negative accumulated temperature. Thus, we screened out the lowest monthly average temperature. Among the climatic indices of growing season length or heat requirements, potential evapotranspiration calculated from the Penman model gained the highest value for both percentage contribution and permutation importance (Table 2). Furthermore, annual average temperature, and potential evapotranspiration calculated by the Thornthwaite model both were strongly correlated with annual extremely low temperature (r = 0.94, P < 0.0001; r = 0.99, P < 0.0001, respectively) (see details in Table 3), and the percentage contribution and permutation importance of other climatic indices of growing season length or heat requirements, such as
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Fig. 4. Responses of the distribution probability of winter wheat to climatic indices.
biotemperature, ≥10 ◦ C interval days, ≥10 ◦ C accumulated temperature, and ≥0 ◦ C interval days were very low (total value < 10%). From the jackknife test, we also found that removing the other climatic indices of growing season length or heat requirements, the simulation result changes little (Fig. 2). Hence we selected only potential evapotranspiration calculated from the Penman model among the climatic indices of growing season length or heat requirements. The percentage contribution and permutation importance of vernalization conditions were very low (total value < 10%), and the gain is very low from jackknife test, they were therefore screened out. Finally, four main climatic indices influencing the distribution of winter wheat cultivation zone were selected: (1) the negative accumulation of daily mean temperature below 0 ◦ C during the winter; (2) the lowest monthly average temperature; (3) potential evapotranspiration calculated from the Penman model; and (4) annual precipitation. These indices also determined the distribution probability of the winter wheat planting zone. 3.2. Simulation of winter wheat cultivation distribution To avoid over-fitting of model, we select 4 climatic indices from 4 groups separately, and guarantee higher simulation accuracy of model, and lower correlation between climatic indices. Based on the main climatic indices including the negative accumulated temperature, the lowest monthly average temperature, the potential evapotranspiration calculated from the Penman model, and annual precipitation, the distribution of the winter wheat cultivation zone was re-simulated using the MaxEnt model. The simulated
result (AUC = 0.870) changes little compared with the former result including the data from all potential climatic indices (AUC = 0.889). This further confirmed that the selected four indices were the main indices determining winter wheat cultivation distribution. The existence probability of winter wheat was calculated using the MaxEnt model. According to the accumulative output of original results from the MaxEnt model, and referring to the method of the evaluation of possibility on IPCC report (IPCC, 2007), the climate suitability of the winter wheat cultivation zone in China could be classified in four categories: unsuitable area (the distribution probability, P < 0.05 (small probability event)), area of low suitability (0.05 ≤ P < 0.33), area of medium suitability (0.33 ≤ P < 0.66), and highly suitable area (P ≥ 0.66) (Fig. 3). 3.3. Threshold of climatic indices determining the limited boundary of winter wheat cultivation The existence probability of winter wheat showed a skewed distribution along with the change in negative accumulated daily mean temperature below 0 ◦ C during winter (Fig. 4A). The latter increased from −700 ◦ C to its maximum value; this indicated that the negative accumulated temperature might be the low temperature threshold index of winter wheat survival. Analyzing the response curve of the existence probability of winter wheat to the annual extremely low temperature (Fig. 4B), we found that the existence probability of winter wheat increased from 0 to a peak value and then declined sharply to 5%, as annual extremely low temperature increased. This indicated that annual extremely low temperature was another overwintering index affecting the distribution of winter wheat, and its threshold was more than −30 ◦ C.
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Fig. 5. Potential distribution of winter wheat in China based on the overwintering index of Yang et al. (2010).
As annual precipitation or potential evapotranspiration increased, the existence probability of winter wheat showed no regularity (Fig. 4C and D). 4. Discussion Low annual extreme temperature can cause the death of winter wheat, and it is overwintering index of winter wheat. However, continuous temperature being higher than annual extreme temperature also leads to winter wheat death, so negative accumulation or the lowest monthly average temperature is another overwintering index of winter wheat. Annual extremely low temperature and the lowest monthly average temperature have been used as the conventional climatic indices considered to constraint the distribution of winter wheat in China (Yang et al., 2010; Zheng, 1981; Zheng and Newman, 1986). The two climatic indices were from long-term field experiment and survey data, and they have not been tested at regional scales. Based the percentage contribution, permutation importance (Table 2), and the jackknife test (Fig. 2) from MaxEnt analysis and the Pearson correlation coefficient matrix (Table 3), the annual extremely low temperature and the negative accumulated temperature were the better overwintering indices. Meanwhile, since the 1990s, culturing techniques have improved, new varieties including ‘winterness wheat’ have been introduced, and these cultivars are able to tolerate lower
temperatures. The lowest tolerable temperature could be as low as −37.5 ◦ C. As a result, winter wheat was able to be planted in Heilongjiang Province (Zu et al., 2001). However, the lowest monthly average temperature (greater than −15 ◦ C), and annual extremely low temperature (greater than −22 to −24 ◦ C) were used in current research (Yang et al., 2010). The existence probability of winter showed that the negative accumulated temperature should be higher than −700 ◦ C, and annual mean extreme minimum temperature should be higher than −30 ◦ C (Fig. 3A and B). Accumulated temperatures such as ≥10 ◦ C interval days, ≥10 ◦ C accumulated temperature, have been usually used as indices of physical regionalization, to distinguish the distribution of different vegetation types. Accumulated temperature has usually been used as an index indicating heat condition for agricultural production. However, this may cause considerable confusion because some very different climatic zones could be recognized as the same climatic zones, or the same climatic zones could be recognized as different climatic zones. Potential evapotranspiration comprises evaporation from all surfaces and plant transpiration. It plays a significant role in physical regionalization, and can be used as a comprehensive climatic index for correlating climate with vegetation and for vegetation classification (Zhou and Zhang, 1996). The results of our MaxEnt model confirmed this. The percentage contribution and permutation importance of other climatic indices such as ≥10 ◦ C interval days, ≥10 ◦ C accumulated temperature, and ≥0 ◦ C
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interval days were very low (total value < 10%). Potential evapotranspiration together with annual precipitation can indicate water supply and affect the distribution of the winter wheat cultivation zone. For comparison with previous work, we created a potential distribution of winter wheat based on the climatic indices from Yang et al. (2010), including the lowest monthly average temperature (greater than −15 ◦ C), and annual extremely low temperature (greater than −22 to −24 ◦ C) (Fig. 5). Former studies indicated that the northeast boundary of winter wheat cultivation zone is the south of Liaoning Province, and our study indicated that the northeast boundary of winter wheat cultivation zone is the north of Heilongjiang Province; Former studies indicated that the northwest boundary of winter wheat cultivation zone is the south of Xinjiang Uygur Autonomous Region, and our study indicated that the northwest boundary of winter wheat cultivation zone is the north of Xinjiang Uygur Autonomous Region. In addition, compared with Yang et al. (2010), the distribution of winter wheat simulated by our MaxEnt model was discontinuous in the southwest of Inner Mongolia, and the east of Gansu Province, Xinjiang Uygur Autonomous Region, and Tsinghai Provinces. This was because we considered temperature constraints as well as limitations in water supply. From the map on climate suitability of winter wheat cultivation zone in China (Fig. 3), the area of medium suitability is close to the major wheat producing provinces (Wang et al., 2009). Based on the meaning of the possibility on IPCC report (IPCC, 2007), the area of medium may indicate the major wheat producing region.
5. Conclusions Winter wheat is one of three major grain crops grown in China. To scientifically map cropping patterns, it is very important to understand the area of its viable cultivation zone in China. Based on the published data, geographical information, national climate data, and the MaxEnt model, the relationship between winter wheat cultivation distribution and climate was established. In order to analyze the variations of north boundary limit of winter wheat in the past or in the future, annual extremely low temperature and the lowest monthly average temperature have been used as the conventional climatic indices considered to constraint the distribution of winter wheat in China (Southworth et al., 2002; Yang et al., 2010; Zheng, 1981; Zheng and Newman, 1986). Based on the analysis of MaxEnt, our studies drew not only that annual extremely low temperature and the negative accumulated temperature were better overwintering indices which mainly determine the north boundary limit of winter wheat, but also the threshold of the overwintering indices. This will make it clear where may be suited to introduce winter wheat in present situation. Based on the overwintering indices and their thresholds, we can predict and analyze exactly the north boundary limit of winter wheat in the past and future. In addition, we introduced two new indices of the potential evapotranspiration and annual precipitation, and make the index system determining the distribution of winter wheat more comprehensive ones in mechanisms. Based on the method of evaluating possibility on IPCC report (IPCC, 2007), the climate suitability of winter wheat cultivation zone in China could be classified in four categories. Our studies showed that the north boundary of medium suitability is the south of Liaoning Province. Former studies did not consider Tibet as a major wheat producing region (Wang et al., 2009), but our studies showed that the southeast of Tibet is the higher suitable area. Winter wheat could be able to cultivate widely to earn more income. Our study describes a suitable winter wheat cultivation zone in China and the northern boundary of winter wheat cultivation. It
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will be helpful for guiding the cultivation of Chinese winter wheat, and for gaining a scientific understanding of the effect of climate change. Acknowledgments This research was jointly supported by the State Key Development Program of Basic Research (2010CB951303), the Special Fund for Agro-scientific Research in the Public Interest (200903003), and the basic operational costs from China Meteorological Administration (CMA). References Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop Evapotranspiration – Guidelines for Computing Crop Water Requirements. FAO Irrigation and Drainage Paper 56. Food and Agriculture Organization of the United Nations, Rome, Italy. Arora, V.K., 2002. The use of the aridity index to assess climate change effect on annual runoff. Journal of Hydrology 265, 164–177. Box, E.O., 1981. Macroclimate and Plant Forms: An Introduction to Predictive Modeling in Phytogeography. Dr. W. Junk Publishers, Hague. Curtis, B.C., 2002. Wheat in the world. In: Curtis, B.C., Rajaram, S., Macpherson, H.G. (Eds.), Bread Wheat Improvement and Production. Plant Production and Protection Series. FAO, Rome, Italy, pp. 1–19. Deng, G.Y., 1993. The Impact of Climate on Agriculture in China. Beijing Science and Technology Press, Beijing (in Chinese). Elith, J., Phillips, S.J., Hastie, T., Dud, M., Chee, Y.E., Yates, C.J., 2011. A statistical explanation of MaxEnt for ecologists. Diversity & Distributions 17, 43–57. Fang, J.Y., Yoda, K., 1990. Climate and vegetation in China III water balance and distribution of vegetation. Ecological Research 5, 9–23. Fielding, A.H., Bell, J.F., 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24 (1), 38–49. Gao, X., Giorgi, F., 2008. Increased aridity in the Mediterranean region under greenhouse gas forcing estimated from high resolution simulations with a regional climate model. Global and Planetary Change 62, 195–209. Harris, S.A., 1973. Comments on the application of the Holdridge system for classification of world life zones as applied to Costa Rica. Arctic and Alpine Research 5 (3), 187–191. He, Z.H., Rajaram, S., Xin, Z.Y., Huang, G.Z., 2001. A History of Wheat Breeding in China. Cimmyt, Mexico. Holdridge, L.R., 1947. Determination of world plant formations from simple climatic data. Science 105, 367–368. IPCC, 2007. In: Core Writing Team, Pachauri, R.K., Reisinger, A. (Eds.), Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Geneva, Switzerland, 104 pp. Jamieson, P.D., Brooking, I.R., Semenov, M.A., Porter, J.R., 1998. Making sense of wheat development: a critique of methodology. Field Crops Research 55, 117–127. Jennie, L.P., Mark, S.B., 2006. Modelling distribution and abundance with presenceonly data. Journal of Applied Ecology 3, 405–412. Köppen, W., 1936. Das geographische System der Klimate. In: Köppen, W., Geiger, R. (Eds.), Handbuch der Klimatologie. Berlin. Bd. Gebr Borntröger I, pp. 1–44. Mather, J.R., Yoshioka, G.A., 1968. The role of climate in the distribution of vegetation. Annals of the Association of American Geographers 58, 29–41. Newman, J.E., 1983. Seasonal Weather Influences on the Size of U.S. Crops. Sta. Bull. 419. Dep. Agronomy, Agricultural Experiment Station, Purdue University, West Lafayette, IN. Mohammad, B., Sarah, S., Amin, A., Sadeghi, S., Azadeh, L.M., 2010. Association between climate indices, aridity index, and rainfed crop yield in northeast of Iran. Field Crops Research 118 (2), 105–114. Peavey, L., 2010. Predicting pelagic habitat with presence-only data using maximum entropy for Olive Ridley Sea Turtles in the eastern tropical Pacific. Master Thesis. Nicholas School of the Environment, Duke University. Peterson, A.T., Papes, M., Eaton, M., 2007. Transferability and model evaluation in ecological niche modeling: a comparison of GARP and MaxEnt. Ecography 30, 550–560. Phillips, S.J., Anderson, R.P., Schapired, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190, 231–259. Phillips, S.J., AT&T Research, 2011. A Brief Tutorial on Maxent. URL: http://www.cs.princeton.edu/∼schapire/maxent/tutorial/tutorial.doc. Porter, J.R., Gawith, M., 1999. Temperatures and the growth and development of wheat: a review. European Journal of Agronomy 10, 23–36. Prentice, I.C., Cramer, W., Harrison, S.P., Leemans, R., Monserud, R.A., Solomon, A.M., 1992. A global biome model based on plant physiology and dominance, soil properties and climate. Journal of Biogeography 19, 117–134. Rosenzweig, C., 1985. Potential CO2 -induced climate effects on North American wheat-producing regions. Climatic Change 7, 367–389. Southworth, J., Pfeifer, R.A., Habeck, M., Randolph, J.C., Doering, O.C., Gangadhar Rao, D., 2002. Sensitivity of winter wheat yields in the Midwestern United States
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Contents lists available at SciVerse ScienceDirect
European Journal of Agronomy journal homepage: www.elsevier.com/locate/eja
Climatic suitability of the distribution of the winter wheat cultivation zone in China Sun Jing-Song a,c , Zhou Guang-Sheng b,a,∗ , Sui Xing-Hua a a b c
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, No. 20 Nanxincun, Xiangshan, Beijing 100093, China Chinese Academy of Meteorological Sciences, Beijing 100081, China Graduate University of Chinese Academy of Sciences, Beijing 100049, China
a r t i c l e
i n f o
Article history: Received 13 October 2011 Received in revised form 20 May 2012 Accepted 21 May 2012 Keywords: China Winter wheat Cultivation zone Climate suitability Distribution
a b s t r a c t Winter wheat is one of major grain crops in China. To scientifically map cropping patterns, it is very important to understand the area of its viable cultivation zone in China. Based on published data, geographical information, national climate data, and the MaxEnt model, the relationship between the distribution of the winter wheat cultivation zone and climate was established. The main indices controlling the distribution of the winter wheat cultivation zone were analyzed to reveal climatic suitability. The main controls on winter wheat distribution in China were: the negative accumulation of daily mean temperatures below 0 ◦ C during winter (i.e., negative accumulated temperature), annual mean extreme minimum temperatures, potential evapotranspiration, and annual precipitation. For winter wheat to safely survive the winter, the negative accumulated temperature should be higher than −700 ◦ C, and the annual mean extreme minimum temperature should be higher than −30 ◦ C. Climate suitability classification of the winter wheat cultivation zone was mapped, based on the MaxEnt probability distribution of winter wheat. Former studies indicated that the northeast boundary of the winter wheat cultivation zone is the south of Liaoning Province, and our study indicated that the northeast boundary of the winter wheat cultivation zone is the north of Heilongjiang Province; former studies indicated that the northwest boundary of the winter wheat cultivation zone is the south of Xinjiang Uygur Autonomous Region, and our study indicated that the northwest boundary of the winter wheat cultivation zone is the north of Xinjiang Uygur Autonomous Region. Our study describes a suitable winter wheat cultivation zone in China and the northern boundary of winter wheat cultivation, and it will be helpful for guiding the cultivation of Chinese winter wheat. Based on our climatic indices and their threshold determining the distribution of winter wheat, it will be helpful for gaining a scientific understanding of the effects of climate change. © 2012 Elsevier B.V. All rights reserved.
1. Introduction China is the largest wheat producer and consumer in the world (Wang et al., 2004). Wheat ranks as the third leading crop in China after rice and maize (Wang et al., 2009). It is thus important to determine the distribution of the winter wheat cultivation zone in order to help guarantee high and stable yields. Wheat is widely distributed from the Arctic to the equator and from lowlands to highlands (Curtis, 2002). However, different wheat cultivars have different heat requirements. Two cultivars predominate: spring wheat and winter wheat, based on seasonal growth and development characteristics. Winter wheat is generally planted in
∗ Corresponding author at: State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, No. 20 Nanxincun, Xiangshan, Beijing 100093, China. Tel.: +86 10 62836268; fax: +86 10 82595962. E-mail address: [email protected] (Z. Guang-Sheng). 1161-0301/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.eja.2012.05.009
September and October (William et al., 2010). Winter wheat may gain higher returns than spring wheat, for four reasons: (1) planting winter wheat can increase the multiple crop index; (2) the quality of winter wheat is better than spring wheat in terms of concentrations of protein and wet gluten; (3) planting winter wheat can gain higher yields due to its longer growing season; (4) planting winter wheat need less seed quantities and can improve yields at low costs (Zhou et al., 2001). Winter wheat is very popular in China because of its high yield, and accounts for around 85% of production and acreage (He et al., 2001). The distribution of the winter wheat cultivation zone in China has interested growing numbers of scientists, and a wealth of literature has been published (Zheng and Newman, 1986; Yang et al., 2010). These studies have determined the north boundary of the cultivation zone according to overwintering heat requirements of winter wheat. The climatic indices of the winter wheat cultivation zone in China have all been based on a bearable low temperature for winter wheat, such as an annual mean extreme minimum
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Table 1 Potential climatic indices affecting the distribution of winter wheat cultivation zone in China. Potential climatic indices Humid index Annual precipitation Penman aridity index Thornthwaite aridity index Holdridge aridity index Growing seasons length or heat required Potential evapotranspiration of Penman model Potential evapotranspiration of Thornthwaite model Potential evapotranspiration of Holdridge Life’s Zones Annual average temperature ≥0 ◦ C interval days ≥10 ◦ C interval days ≥0 ◦ C accumulated temperature ≥10 ◦ C accumulated temperature Biotemperature Winter index Annual extremely low temperature Lowest monthly average temperature negative accumulation of daily mean temperature < 0 ◦ C during the winter Vernalization index Lowest monthly average temperature Duration in days of 0–3 ◦ C Duration in days of 0–7 ◦ C
Abbreviation
Refs.
Annual precipitation Penman AI Thornthwaite AI Holdridge AI
1–7,11–14 7,8 7,8 7
Penman PET Thornthwaite PET Holdridge PET Annual average temperature ≥0 ◦ C interval days ≥10 ◦ C interval days ≥0 ◦ C accumulated temperature ≥10 ◦ C accumulated temperature Biotemperature
7,8 7,8 7,10 1,4,8 5 1 5 1 4,6,8,9,10
Annual extremely low temperature Lowest monthly average temperature Negative accumulated temperature
11,12 1,2,11,16 14,17
Lowest monthly average temperature 0–3 ◦ C vernalization days 0–7 ◦ C vernalization days
18 15 15
Refs. [1] Yan et al. (2011); [2] Prentice et al. (1992); [3] Köppen (1936); [4] Whittaker (1975); [5] Box (1981); [6] Holdridge (1947); [7] Gao and Giorgi (2008); [8] Mohammad et al. (2010); [9] Mather and Yoshioka (1968); [10] Harris (1973); [11] Yang et al. (2010); [12] Porter and Gawith (1999); [13] Zheng (1981); [14] Zheng and Newman (1986); [15] Deng (1993); [16] Yun et al. (2007); [17] Newman (1983); [18] William et al. (2010).
temperature, the lowest monthly average temperature, or the negative accumulation of daily mean temperature below 0 ◦ C during winter (i.e., negative accumulated temperature) (Newman, 1983; Porter and Gawith, 1999; Rosenzweig, 1985; Veisz et al., 1996; Yang et al., 2010, 2011; Zheng and Newman, 1986). Based on above overwintering indices mentioned, studies showed that the distribution of winter wheat has been limited in northern but not in southern China (Yun et al., 2007). According to the distribution mechanism of plant species, there may be three types of climatic indices used to determine winter wheat distribution: (1) the minimum temperature that a plant can tolerate; (2) the growing season length and heat requirements for completion of its life cycle; (3) the water supply necessary for plant canopy formation and maintenance (Woodward, 1987). Former studies have mainly focused on the first point, the minimum temperature that plant can tolerate (Newman, 1983; Porter and Gawith, 1999; Rosenzweig, 1985; Veisz et al., 1996; Yang et al., 2011; Zheng and Newman, 1986), fewer studies have paid attention to the length of the growing season (i.e., the heat required), or to the water supply. Under double cropping systems, where winter wheat and other crops occupy the whole growing season, the effects of growing season length and heat requirements should not be ignored. Meanwhile, the water supply for plant canopy formation and maintenance becomes the key factor determining the distribution of winter wheat in the arid and semi-arid areas of North China. Both areas with too much precipitation (more than the water requirement for winter wheat development) and too little precipitation (not enough for winter wheat development) will decrease winter wheat yield. Growth and developmental phases of wheat are controlled by vernalization (William et al., 2010), so the climatic indices of vernalization have not been ignored in the literature. The main short-comings of the former studies were: (1) former climate indices have been based on small areas and small experiments, and have lacked objectivity, quantification, and technical explanation (e.g., the distribution mechanism of plant species as mentioned above); (2) these climatic indices have not been tested at regional scales, so different researchers have designed different
climatic indices as they have focused on differing spatial and temporal scales; and (3) former studies have paid more attention to changes in the northern boundary of winter wheat planting, rather than conducting fine analyses at regional scales. We considered the published literature and the development of different winter wheat cultivars in different regions, and gathered the potential climatic indices affecting the distribution of the winter wheat at regional and annual scales. The MaxEnt model is a method to evaluate species distribution when presence-only data exist, and it was successfully applied for predicting species invasion, plant diseases and insect pests, species conservation and so on (Phillips et al., 2006; Peavey, 2010), and therefore, we revealed the major climatic indices of the distribution of winter wheat cultivation zone, using a MaxEnt model, and simulated the MaxEnt probability distribution of winter wheat in China. Our objectives were: (1) to reveal the major climatic indices of winter wheat distribution and obtain the MaxEnt probability distribution of winter wheat in China; (2) to evaluate the climatic suitability of winter wheat cultivation distribution in China; and (3) to determine the threshold values for the main climatic indices affecting winter wheat cultivation distribution in China. 2. Material and methods 2.1. Selection of climatic indices Because of diverse phenophases of winter wheat with different cultivation areas, cultivation habits, and varieties (Jamieson et al., 1998), it is difficult to explain climatic indices affecting the distribution of the winter wheat at shorter time scales. Based on this and our target of exploring the distribution of winter wheat at national level, the climatic indices should be selected at regional and annual scales. Considering mechanisms of plant species distribution may apply to winter wheat, the selection of indices was not only based on the characteristics of winter wheat growth, but also the mechanisms of plant species distribution. Based on the above principles about selecting climatic indices and the published data, the climatic indices affecting the distribution of the winter wheat cultivation
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Fig. 1. Distribution of winter wheat (A) and weather station (B) in China.
Table 2 Contributions of the climatic indices to the simulation results of the MaxEnt model. Variables abbreviations followed Table 1. Variable
Percent contribution
Permutation importance
Negative accumulated temperature Lowest monthly average temperature Penman PET Annual average temperature Annual precipitation Holdridge AI 0–3 ◦ C vernalization days ≥10 ◦ C interval days 0–7 ◦ C vernalization days Annual extremely low temperature Penman AI Biotemperature Thornthwaite AI ≥0 ◦ C accumulated temperature ≥10 ◦ C accumulated temperature ≥0 ◦ C interval days Thornthwaite PET Holdridge PET
26.1 19.2 10.4 9.6 6.9 6.7 6.7 3.6 2.7 2.1 1.6 1.4 1.2 0.6 0.6 0.3 0.1 0.1
41.3 0.3 11.2 0 14.3 0.4 4.3 2.2 1.7 14 4.7 0 0.4 0.7 3.3 0.1 0.8 0.2
zone can be classified to four types: (1) the minimum temperature that winter wheat can tolerate (winter index); (2) the growing season length or heat requirements for winter wheat to complete a life cycle; (3) water supply for winter wheat canopy formation and maintenance (humidity index); and (4) vernalization index of winter wheat (see details in Table 1). 2.2. Data processing Our data included geographic information about the distribution of winter wheat in China (1991–2007) (Fig. 1A), and daily climate data (1978–2007) from China’s ground climate data (Fig. 1B). These data were collected by National Meteorological Information Center, China Meteorological Administration. For fine grid analysis, the data containing daily temperature, precipitation, wind, and humidity were interpolated onto the surface grid data using a 10 km × 10 km resolution, following Thornton et al. (1997). We gained the surface data of solar radiation following the methods of Thornton and Running (1999). The aridity index (AI) (see details in Table 1, humid index) was calculated using AI = PET/P, where PET is the potential evapotranspiration, and P is the annual precipitation (Arora, 2002). There are three conventional calculations used to determine potential
evapotranspiration: the Penman model, the Thornthwaite model, and the Holdridge Life Zones system (Allen et al., 1998; Fang and Yoda, 1990). We used all three and compared results. We used the biotemperature used in the categorizing system of the Holdridge Life Zones (biotemperature is similar to the growing degree days, defined as the summation of monthly averaged temperature between 0 ◦ C and 30 ◦ C) (Holdridge, 1947). For eliminating the effects of random fluctuation of mean daily air temperature, immobile beginning and ending dates of ≥0 ◦ C and ≥10 ◦ C each year were determined with 5-day moving average method (Yan et al., 2011). 2.3. The MaxEnt model The MaxEnt model is a program for simulating species distribution when presence-only data exist (Phillips et al., 2006). Presence-only data, for which there is no information on locations where the species is absent, are common in both animal and plant studies. In many situations, these may be the only data available on a species. We need effective ways to use these data to explore species distribution or species use of habitat (Jennie and Mark, 2006). The MaxEnt model is able to predict the species’ distributions in broad unsampled regions and can explain results according
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Table 3 The Pearson correlation coefficient matrix between the climatic indices. ≥0 ◦ C accumulated temperature ◦
≥0 C accumulated temperature ≥0 ◦ C interval days ≥10 ◦ C accumulated temperature ≥10 ◦ C interval days 0–3 ◦ C vernalization temperature 0–7 ◦ C vernalization temperature Annual average temperature Annual extremely low temperature Annual precipitation Biotemperature Holdridge AI Holdridge PET Lowest monthly average temperature Negative accumulated temperature Penman AI Penman PET Thornthwaite AI Thornthwaite PET
≥0 ◦ C accumulated temperature ≥0 ◦ C interval days ≥10 ◦ C accumulated temperature ≥10 ◦ C interval days 0–3 ◦ C vernalization temperature 0–7 ◦ C vernalization temperature Annual average temperature Annual extremely low temperature Annual precipitation Biotemperature Holdridge AI Holdridge PET Lowest monthly average temperature Negative accumulated temperature Penman AI Penman PET Thornthwaite AI Thornthwaite PET
≥0 ◦ C interval days
1
**
.890 1 .866** .913** −.201** −.105** .953** .934** .786** .890** −.183** .890** .920** .938** −.504** .550** −.234** .897**
.890** .997** .946** −.539** −.452** .975** .883** .803** 1.000** −.091** 1.000** .864** .781** −.496** .417** −.156** .977**
≥10 ◦ C interval days .946** .913** .939** 1 −.515** −.411** .942** .865** .797** .946** −.132** .946** .842** .798** -.532** .410** −.199** .916**
.997 .866** 1 .939** −.572** −.511** .961** .855** .785** .997** −.075** .997** .835** .754** −.499** .384** −.143** .967**
0–3 ◦ C vernalization temperature
0–7 ◦ C vernalization temperature
Annual average temperature
Annual extremely low temperature
−.539** −.201** −.572** −.515** 1 .877** −.388** −.296** −.468** −.539** .089** −.539** −.279** −.040** .262** −.068** .130** −.485**
−.452** −.105** −.511** −.411** .877** 1 −.296** −.124** −.273** −.452** −.048** −.452** −.114** .015** .257** .096** .013** −.369**
.975** .953** .961** .942** −.388** −.296** 1 .943** .788** .975** −.078** .975** .929** .898** −.463** .512** −.133** .975**
.883** .934** .855** .865** −.296** −.124** .943** 1 .831** .883** −.209** .883** .993** .893** −.374** .726** −.228** .941**
Annual precipitation ◦
≥0 C accumulated temperature ≥0 ◦ C interval days ≥10 ◦ C accumulated temperature ≥10 ◦ C interval days 0–3 ◦ C vernalization temperature 0–7 ◦ C vernalization temperature Annual average temperature Annual extremely low temperature Annual precipitation Biotemperature Holdridge AI Holdridge PET Lowest monthly average temperature Negative accumulated temperature Penman AI Penman PET Thornthwaite AI Thornthwaite PET
≥0 ◦ C accumulated temperature ≥0 ◦ C interval days ≥10 ◦ C accumulated temperature ≥10 ◦ C interval days 0–3 ◦ C vernalization temperature 0–7 ◦ C vernalization temperature Annual average temperature Annual extremely low temperature Annual precipitation Biotemperature Holdridge AI
≥10 ◦ C accumulated temperature
**
Biotemperature
**
**
.803 .786** .785** .797** −.468** −.273** .788** .831** 1 .803** −.530** .803** .828** .633** −.587** .608** −.563** .845**
1.000 .890** .997** .946** −.539** −.452** .975** .883** .803** 1 −.091** 1.000** .864** .781** −.496** .417** −.156** .977**
Holdridge AI
Holdridge PET
−.091 −.183** −.075** −.132** .089** −.048** −.078** −.209** −.530** −.091** 1 −.091** −.217** −.053** .586** −.303** .987** −.150**
1.000** .890** .997** .946** −.539** −.452** .975** .883** .803** 1.000** −.091** 1 .864** .781** −.496** .417** −.156** .977**
**
Lowest monthly average temperature
Negative accumulated temperature
Penman AI
Penman PET
Thornthwaite AI
.864** .920** .835** .842** −.279** −.114** .929** .993** .828** .864** −.217**
.781** .938** .754** .798** −.040** .015** .898** .893** .633** .781** −.053**
−.496** −.504** −.499** −.532** .262** .257** −.463** −.374** −.587** −.496** .586**
.417** .550** .384** .410** −.068** .096** .512** .726** .608** .417** −.303**
−.156** −.234** −.143** −.199** .130** .013** −.133** −.228** −.563** −.156** .987**
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Table 3 ( continued )
Holdridge PET Lowest monthly average temperature Negative accumulated temperature Penman AI Penman PET Thornthwaite AI Thornthwaite PET **
Lowest monthly average temperature
Negative accumulated temperature
Penman AI
Penman PET
Thornthwaite AI
.864** 1 .891** −.347** .773** −.230** .935**
.781** .891** 1 −.386** .568** −.090** .814**
−.496** −.347** −.386** 1 −.003 .698** −.436**
.417** .773** .568** −.003 1 −.248** .576**
−.156** −.230** −.090** .698** −.248** 1 −.192**
P < 0.01.
to habitat (Peterson et al., 2007). Compared MaxEnt predictions with those of a commonly used presence-only modeling method, the Genetic Algorithm for Rule-Set Prediction (GARP), the area under the ROC curve (AUC) was almost always higher for MaxEnt, indicating better discrimination of suitable versus unsuitable areas for the species (Elith et al., 2011). Prediction by the MaxEnt model can be assessed using a receiver operating curve (ROC). A ROC plot is obtained by plotting all sensitivity values (true positive fraction) on the y axis against their equivalent (1– specificity) values (false positive fraction) for all available thresholds on the x axis. The area under the ROC function (AUC) is usually taken to be an important index because it provides a single measure of overall accuracy that is not dependent upon a particular threshold. The value of the AUC is between 0.5 and 1.0. The higher the value, the better the simulated results; and if the value was lower than 0.50, the simulated results were considered to make no sense (Fielding and Bell, 1997). If AUC > 0.78, the model performed well (Peavey, 2010). There are two methods to assess the contributions of climate indices to simulated models (Phillips and AT&T Research, 2011): (1) percentage contribution and permutation importance and (2) the jackknife test. While the MaxEnt model is being trained, it keeps track of which environmental variables are contributing to fitting the model. Each step of the MaxEnt algorithm increases the gain of the model by modifying the coefficient for a single feature; the program assigns the increase in the gain to the environmental variable
that the feature depends on. Converting to percentages at the end of the training process, we get the middle column in Table 2. These percent contribution values are only heuristically defined: they depend on the particular path that the MaxEnt code uses to get to the optimal solution, and a different algorithm could get to the same solution via a different path, resulting in different percent contribution values. In addition, when there are highly correlated environmental variables, the percent contributions should be interpreted with caution. The right-hand column in the table shows a second measure of variable contributions, called permutation importance. This measure depends only on the final MaxEnt model, not the path used to obtain it. The contribution for each variable is determined by randomly permuting the values of that variable among the training points (both presence and background) and measuring the resulting decrease in training AUC. A large decrease indicates that the model depends heavily on that variable. Values are normalized to give percentages. To get alternate estimates of variable importance, we can also run a jackknife test. When we run a jackknife test, a number of models are created. Each variable is excluded in turn, and a model created with the remaining variables. Then a model is created using each variable in isolation. In addition, a model is created using all variables, as before. The jackknife test is illustrated by a bar graph of black, hollow, and light gray bars (Fig. 2). The length of the hollow bar at the bottom represents the total score for simulating
Fig. 2. The bar chart of jackknife test.
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Fig. 3. Climate suitability of winter wheat cultivation zone in China.
the distribution of winter wheat using all the selected potential climatic indices. The length of the black bar represents the score using only one of the climatic indices; the longer the bar, the more important is the climatic factor. The length of the light gray bar represents the score of a model created with the remaining indices. The greater the difference in length between light gray and hollow bars, the less likely that the factor affecting winter wheat distribution can be replaced by other indices. The factor can only be replaced by other indices if the hollow and light gray bars show similar lengths. We used default parameters for the MaxEnt model (convergence threshold of 10−5 , the maximum iteration value of 1000 and automatic regularization with a value of 10−4 ), and the cumulative output format of the original results.
3. Results 3.1. Selection of the main climatic indices affecting winter wheat cultivation distribution Low annual extreme temperature can cause the death of winter wheat, and it is overwintering index of winter wheat. The annual extremely low temperature had a higher permutation importance, and the model depended heavily on this factor (Table 2). However, continuous temperature being higher than annual extreme temperature also leads to winter wheat death, so negative
accumulation or the lowest monthly average temperature is another overwintering index of winter wheat. A negative accumulated daily mean temperature of below 0 ◦ C during the winter, gained the highest values for percentage contribution and permutation importance (Table 2), and the longest black bar (in the jackknife test) (Fig. 2). At the same time, the permutation importance of the lowest monthly average temperature was very low (Table 2), the Pearson correlation coefficient matrix between the climatic indices showed that the lowest monthly average temperature was significantly strongly related to the negative accumulated temperature (r = 0.89, P < 0.0001), and the annual extremely low temperature (r = 0.99, P < 0.0001) (see details in Table 3). Based on this analysis, the information about the distribution of winter wheat provided by the lowest monthly average temperature could be replaced by annual extremely low temperature and negative accumulated temperature. Thus, we screened out the lowest monthly average temperature. Among the climatic indices of growing season length or heat requirements, potential evapotranspiration calculated from the Penman model gained the highest value for both percentage contribution and permutation importance (Table 2). Furthermore, annual average temperature, and potential evapotranspiration calculated by the Thornthwaite model both were strongly correlated with annual extremely low temperature (r = 0.94, P < 0.0001; r = 0.99, P < 0.0001, respectively) (see details in Table 3), and the percentage contribution and permutation importance of other climatic indices of growing season length or heat requirements, such as
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Fig. 4. Responses of the distribution probability of winter wheat to climatic indices.
biotemperature, ≥10 ◦ C interval days, ≥10 ◦ C accumulated temperature, and ≥0 ◦ C interval days were very low (total value < 10%). From the jackknife test, we also found that removing the other climatic indices of growing season length or heat requirements, the simulation result changes little (Fig. 2). Hence we selected only potential evapotranspiration calculated from the Penman model among the climatic indices of growing season length or heat requirements. The percentage contribution and permutation importance of vernalization conditions were very low (total value < 10%), and the gain is very low from jackknife test, they were therefore screened out. Finally, four main climatic indices influencing the distribution of winter wheat cultivation zone were selected: (1) the negative accumulation of daily mean temperature below 0 ◦ C during the winter; (2) the lowest monthly average temperature; (3) potential evapotranspiration calculated from the Penman model; and (4) annual precipitation. These indices also determined the distribution probability of the winter wheat planting zone. 3.2. Simulation of winter wheat cultivation distribution To avoid over-fitting of model, we select 4 climatic indices from 4 groups separately, and guarantee higher simulation accuracy of model, and lower correlation between climatic indices. Based on the main climatic indices including the negative accumulated temperature, the lowest monthly average temperature, the potential evapotranspiration calculated from the Penman model, and annual precipitation, the distribution of the winter wheat cultivation zone was re-simulated using the MaxEnt model. The simulated
result (AUC = 0.870) changes little compared with the former result including the data from all potential climatic indices (AUC = 0.889). This further confirmed that the selected four indices were the main indices determining winter wheat cultivation distribution. The existence probability of winter wheat was calculated using the MaxEnt model. According to the accumulative output of original results from the MaxEnt model, and referring to the method of the evaluation of possibility on IPCC report (IPCC, 2007), the climate suitability of the winter wheat cultivation zone in China could be classified in four categories: unsuitable area (the distribution probability, P < 0.05 (small probability event)), area of low suitability (0.05 ≤ P < 0.33), area of medium suitability (0.33 ≤ P < 0.66), and highly suitable area (P ≥ 0.66) (Fig. 3). 3.3. Threshold of climatic indices determining the limited boundary of winter wheat cultivation The existence probability of winter wheat showed a skewed distribution along with the change in negative accumulated daily mean temperature below 0 ◦ C during winter (Fig. 4A). The latter increased from −700 ◦ C to its maximum value; this indicated that the negative accumulated temperature might be the low temperature threshold index of winter wheat survival. Analyzing the response curve of the existence probability of winter wheat to the annual extremely low temperature (Fig. 4B), we found that the existence probability of winter wheat increased from 0 to a peak value and then declined sharply to 5%, as annual extremely low temperature increased. This indicated that annual extremely low temperature was another overwintering index affecting the distribution of winter wheat, and its threshold was more than −30 ◦ C.
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Fig. 5. Potential distribution of winter wheat in China based on the overwintering index of Yang et al. (2010).
As annual precipitation or potential evapotranspiration increased, the existence probability of winter wheat showed no regularity (Fig. 4C and D). 4. Discussion Low annual extreme temperature can cause the death of winter wheat, and it is overwintering index of winter wheat. However, continuous temperature being higher than annual extreme temperature also leads to winter wheat death, so negative accumulation or the lowest monthly average temperature is another overwintering index of winter wheat. Annual extremely low temperature and the lowest monthly average temperature have been used as the conventional climatic indices considered to constraint the distribution of winter wheat in China (Yang et al., 2010; Zheng, 1981; Zheng and Newman, 1986). The two climatic indices were from long-term field experiment and survey data, and they have not been tested at regional scales. Based the percentage contribution, permutation importance (Table 2), and the jackknife test (Fig. 2) from MaxEnt analysis and the Pearson correlation coefficient matrix (Table 3), the annual extremely low temperature and the negative accumulated temperature were the better overwintering indices. Meanwhile, since the 1990s, culturing techniques have improved, new varieties including ‘winterness wheat’ have been introduced, and these cultivars are able to tolerate lower
temperatures. The lowest tolerable temperature could be as low as −37.5 ◦ C. As a result, winter wheat was able to be planted in Heilongjiang Province (Zu et al., 2001). However, the lowest monthly average temperature (greater than −15 ◦ C), and annual extremely low temperature (greater than −22 to −24 ◦ C) were used in current research (Yang et al., 2010). The existence probability of winter showed that the negative accumulated temperature should be higher than −700 ◦ C, and annual mean extreme minimum temperature should be higher than −30 ◦ C (Fig. 3A and B). Accumulated temperatures such as ≥10 ◦ C interval days, ≥10 ◦ C accumulated temperature, have been usually used as indices of physical regionalization, to distinguish the distribution of different vegetation types. Accumulated temperature has usually been used as an index indicating heat condition for agricultural production. However, this may cause considerable confusion because some very different climatic zones could be recognized as the same climatic zones, or the same climatic zones could be recognized as different climatic zones. Potential evapotranspiration comprises evaporation from all surfaces and plant transpiration. It plays a significant role in physical regionalization, and can be used as a comprehensive climatic index for correlating climate with vegetation and for vegetation classification (Zhou and Zhang, 1996). The results of our MaxEnt model confirmed this. The percentage contribution and permutation importance of other climatic indices such as ≥10 ◦ C interval days, ≥10 ◦ C accumulated temperature, and ≥0 ◦ C
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interval days were very low (total value < 10%). Potential evapotranspiration together with annual precipitation can indicate water supply and affect the distribution of the winter wheat cultivation zone. For comparison with previous work, we created a potential distribution of winter wheat based on the climatic indices from Yang et al. (2010), including the lowest monthly average temperature (greater than −15 ◦ C), and annual extremely low temperature (greater than −22 to −24 ◦ C) (Fig. 5). Former studies indicated that the northeast boundary of winter wheat cultivation zone is the south of Liaoning Province, and our study indicated that the northeast boundary of winter wheat cultivation zone is the north of Heilongjiang Province; Former studies indicated that the northwest boundary of winter wheat cultivation zone is the south of Xinjiang Uygur Autonomous Region, and our study indicated that the northwest boundary of winter wheat cultivation zone is the north of Xinjiang Uygur Autonomous Region. In addition, compared with Yang et al. (2010), the distribution of winter wheat simulated by our MaxEnt model was discontinuous in the southwest of Inner Mongolia, and the east of Gansu Province, Xinjiang Uygur Autonomous Region, and Tsinghai Provinces. This was because we considered temperature constraints as well as limitations in water supply. From the map on climate suitability of winter wheat cultivation zone in China (Fig. 3), the area of medium suitability is close to the major wheat producing provinces (Wang et al., 2009). Based on the meaning of the possibility on IPCC report (IPCC, 2007), the area of medium may indicate the major wheat producing region.
5. Conclusions Winter wheat is one of three major grain crops grown in China. To scientifically map cropping patterns, it is very important to understand the area of its viable cultivation zone in China. Based on the published data, geographical information, national climate data, and the MaxEnt model, the relationship between winter wheat cultivation distribution and climate was established. In order to analyze the variations of north boundary limit of winter wheat in the past or in the future, annual extremely low temperature and the lowest monthly average temperature have been used as the conventional climatic indices considered to constraint the distribution of winter wheat in China (Southworth et al., 2002; Yang et al., 2010; Zheng, 1981; Zheng and Newman, 1986). Based on the analysis of MaxEnt, our studies drew not only that annual extremely low temperature and the negative accumulated temperature were better overwintering indices which mainly determine the north boundary limit of winter wheat, but also the threshold of the overwintering indices. This will make it clear where may be suited to introduce winter wheat in present situation. Based on the overwintering indices and their thresholds, we can predict and analyze exactly the north boundary limit of winter wheat in the past and future. In addition, we introduced two new indices of the potential evapotranspiration and annual precipitation, and make the index system determining the distribution of winter wheat more comprehensive ones in mechanisms. Based on the method of evaluating possibility on IPCC report (IPCC, 2007), the climate suitability of winter wheat cultivation zone in China could be classified in four categories. Our studies showed that the north boundary of medium suitability is the south of Liaoning Province. Former studies did not consider Tibet as a major wheat producing region (Wang et al., 2009), but our studies showed that the southeast of Tibet is the higher suitable area. Winter wheat could be able to cultivate widely to earn more income. Our study describes a suitable winter wheat cultivation zone in China and the northern boundary of winter wheat cultivation. It
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