中南半岛稻谷潜在分布区模拟

doi:10.18402/resci.2021.12.01

摘要/Abstract

摘要：

农作物潜在分布区是掌握农作物分布范围、估算农作物产量、评估气候变化对农业生产的影响、规划调整农业种植结构,以及规避自然和市场风险等的重要参考。中南半岛是全球水稻种植最为集中的地区之一,对全球粮食安全意义重大。本文采用物种分布模型MaxEnt,对中南半岛水稻的潜在分布区进行模拟。研究结果显示：①高程、坡度、灌溉距离成本、等温性、年温度变幅、年降雨量、降水变异系数和最暖季降雨量等8个环境与气候因子是该地区水稻分布的主要影响因素;②将水稻样本按照种植类型分为3类（低地灌溉、低地雨养和高地雨养）进行模拟比利用全部样本进行模拟的精度显著提高;③中南半岛水稻的潜在分布区为1.140亿hm²,占全部国土面积的53.3%。本文模拟的潜在分布区可以为指引中南半岛、特别是澜沧江—湄公河流域水稻种植,保障区域粮食供给和优化农业产业布局提供科学支撑。

关键词: 水稻种植, 潜在分布区, 物种分布模拟, 最大熵模型（MaxEnt）, 粮食安全, 中南半岛

Abstract:

Potential distribution area plays important roles for estimating crop planting range and production, evaluating the impact of climate change on agricultural production, planning agricultural structure, and avoiding natural and market risks, among others. The Indo-China Peninsula (ICP) is a dominant region for rice production and plays a vital role for the food security of the world. This study modeled the potential distribution area of rice production across the ICP using MaxEnt—a popular software for species distribution modeling. The results show that: (1) Eight environmental factors, i.e, elevation, slope, irrigation cost distance, isothermality, temperature annual range, annual precipitation, precipitation seasonality and precipitation of warmest quarter, are the main influencing factors for rice production distribution in the region; (2) The model accuracy increased significantly (p<0.05) when all rice samples were divided into three subgroups (lowland irrigation, lowland rainfed, and upland rainfed) before modeling; (3) The potential distribution area of rice production in the ICP is 114 million hectares, which accounts for 53.3% of the total land area of the region. The results of the potential distribution area of rice production can offer scientific supports for guiding rice plantation pattern, safeguarding regional food supply, and optimizing the agricultural industry in the ICP region.

Key words: rice production, potential distribution area, species distribution modeling, MaxEnt, food security, Indo-China Peninsula

吴良, 沈镭, 成升魁. 中南半岛稻谷潜在分布区模拟[J]. 资源科学, 2021, 43(12): 2369-2380.

WU Liang, SHEN Lei, CHENG Shengkui. Potential distribution modeling of rice production in the Indo-China Peninsula[J]. Resources Science, 2021, 43(12): 2369-2380.

图/表 6

图1

表1

环境变量概况及其对模型的贡献率

名称	定义或计算方法	单位	对模型的贡献率/%
名称	定义或计算方法	单位	全部	低地灌溉	低地雨养	高地雨养
高程（DEM）	海拔高度（GTOPO30）	m	9.0	75.0	57.8	54.7
坡度（slope）	坡度= $垂直高度水平方向的距离$	°	15.2	13.2	14.0	7.0
灌溉距离成本（costdist）	地面单元离河流的最近距离	km	12.5	3.4	13.6	4.6
等温性（bio3）	bio3= $每月温度最大变幅的平均每年温度最大变幅 × 100$	-	17.9	0.9	2.9	6.1
温度变幅（bio7）	bio7=最暖月最高气温-最冷月最低气温	℃	19.2	2.9	6.1	5.6
年降雨量（bio12）	多年平均降雨量	mm	9.8	1.3	1.3	1.2
降水变异系数（bio15）	每月降水量变异系数	-	1.3	3.2	1.1	6.2
最暖季降雨量（bio18）	温度最高季节所有月份降雨量之和	mm	15.0	0.2	3.2	14.6

表1

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图4

图5

参考文献 38

[1]	Soberón J, Nakamura M. Niches and distributional areas: Concepts, methods, and assumptions[J]. Proceedings of the National Academy of Sciences, 2009, 106:19644-19650. doi: 10.1073/pnas.0901637106
[2]	武晋雯, 孙龙彧, 纪瑞鹏, 等. 辽宁水稻气候适宜度日尺度评价研究[J]. 资源科学, 2017, 39(8):1605-1613.
	[ Wu J W, Sun L Y, Ji R P, et al. Intraday evaluation modeling of climatic suitability for rice in Liaoning Province[J]. Resources Science, 2017, 39(8):1605-1613.]
[3]	罗海平, 邹楠, 胡学英, 等. 1980-2019年中国粮食主产区主要粮食作物气候生产潜力与气候资源利用效率[J]. 资源科学, 2021, 43(6):1234-1247. doi: 10.18402/resci.2021.06.14
	[ Luo H P, Zou N, Hu X Y, et al. Climatic potential productivity and resources utilization efficiency of major grain crops in the main grain production areas of China, 1980-2019[J]. Resources Science, 2021, 43(6):1234-1247.]
[4]	Grinnell J. Barriers to distribution as regards birds and mammals[J]. The American Naturalist, 1914, 48:248-254. doi: 10.1086/279402
[5]	Hutchinson G E. Concluding remarks on the cold spring harbor symposia on quantitative biology[J]. Cold Spring Harbor Symposia on Quantitative Biology, 1957, 22:415-427. doi: 10.1101/SQB.1957.022.01.039
[6]	Guisan A, Thuiller W. Predicting species distribution: Offering more than simple habitat models[J]. Ecology Letters, 2005, 8(9):993-1009. doi: 10.1111/ele.2005.8.issue-9
[7]	Elith J, Leathwick J R. Species distribution models: Ecological explanation and prediction across space and time[J]. Annual Review of Ecology, Evolution, and Systematics, 2009, 40(1):677-697. doi: 10.1146/ecolsys.2009.40.issue-1
[8]	Estes L D, Bradley B A, Beukes H, et al. Comparing mechanistic and empirical model projections of crop suitability and productivity: Implications for ecological forecasting[J]. Global Ecology and Biogeography, 2013, 22(8):1007-1018. doi: 10.1111/geb.12034
[9]	Hoogenboom G, Porter C H, Boote K J, et al. The DSSAT Crop Modeling Ecosystem[A]. Boote K J. Advances in Crop Modeling for a Sustainable Agriculture[C]. Cambridge: Burleigh Dodds Science Publishing, 2019.
[10]	Lobell D B. The use of satellite data for crop yield gap analysis[J]. Field Crops Research, 2013, 143:56-64. doi: 10.1016/j.fcr.2012.08.008
[11]	Wu B F, Meng J H, Li Q Z, et al. Remote sensing-based global crop monitoring: Experiences with China’s CropWatch system[J]. International Journal of Digital Earth, 2014, 7(2):113-137. doi: 10.1080/17538947.2013.821185
[12]	Franklin J, Miller J A. Mapping Species Distributions: Spatial Inference and Prediction[M]. Cambridge: Cambridge University Press, 2010.
[13]	Akpoti K, Dossou-Yovo E R, Zwart S J, et al. The potential for expansion of irrigated rice under alternate wetting and drying in Burkina Faso[J]. Agricultural Water Management, 2021, DOI: 10.1016/j.agwat.2021.106758. doi: 10.1016/j.agwat.2021.106758
[14]	Akpoti K, Kabo-bah A T, Dossou-Yovo E R, et al. Mapping suitability for rice production in inland valley landscapes in Benin and Togo using environmental niche modeling[J]. Science of the Total Environment, 2020, 709:1-19.
[15]	Mohammed I, Marshall M, de Bie K, et al. A blended census and multiscale remote sensing approach to probabilistic cropland mapping in complex landscapes[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 161:233-245. doi: 10.1016/j.isprsjprs.2020.01.024
[16]	Phillips S J, Dudik M. Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation[J]. Ecography, 2008, 31(2):161-175. doi: 10.1111/j.0906-7590.2008.5203.x
[17]	Elith J, Phillips S J, Hastie T, et al. A statistical explanation of MaxEnt for ecologists[J]. Diversity and Distributions, 2011, 17(1):43-57. doi: 10.1111/ddi.2010.17.issue-1
[18]	Phillips S J, Anderson R P, Schapire R E. Maximum entropy modeling of species geographic distributions[J]. Ecological Modelling, 2006, 190(3):231-259. doi: 10.1016/j.ecolmodel.2005.03.026
[19]	Elith J, Graham C H, Anderson R P, et al. Novel methods improve prediction of species’ distributions from occurrence data[J]. Ecography, 2006, 29(2):129-151. doi: 10.1111/j.2006.0906-7590.04596.x
[20]	Radosavljevic A, Anderson R P. Making better MAXENT models of species distributions: Complexity, overfitting and evaluation[J]. Journal of Biogeography, 2014, 41(4):629-643. doi: 10.1111/jbi.12227
[21]	Hu W J, Wang Y Y, Zhang D, et al. Mapping the potential of mangrove forest restoration based on species distribution models: A case study in China[J]. Science of the Total Environment, 2020, 748:142321. doi: 10.1016/j.scitotenv.2020.142321
[22]	Singh M, Arunachalam R, Kumar L. Modeling potential hotspots of invasive Prosopisjuliflora (Swartz) DC in India[J]. Ecological Informatics, 2021, DOI: 10.1016/j.ecoinf.2021.101386. doi: 10.1016/j.ecoinf.2021.101386
[23]	Chhogyel N, Kumar L, Bajgai Y, et al. Prediction of Bhutan’s ecological distribution of rice (Oryza sativa L. ) under the impact of climate change through maximum entropy modelling[J]. The Journal of Agricultural Science, 2020, 158(1):25-37. doi: 10.1017/S0021859620000350
[24]	Dang A T N, Kumar L, Reid M. Modelling the potential impacts of climate change on rice cultivation in Mekong Delta, Vietnam[J]. Sustainability, 2020, DOI: 10.3390/su12229608. doi: 10.3390/su12229608
[25]	Beck J, Boller M, Erhardt A, et al. Spatial bias in the GBIF database and its effect on modeling species’ geographic distributions[J]. Ecological Informatics, 2014, 19:10-15. doi: 10.1016/j.ecoinf.2013.11.002
[26]	Bystriakova N, Peregrym M, Erkens R H J, et al. Sampling bias in geographic and environmental space and its effect on the predictive power of species distribution models[J]. Systematics and Biodiversity, 2012, 10(3):305-315. doi: 10.1080/14772000.2012.705357
[27]	Fick S E, Hijmans R J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas[J]. International Journal of Climatology, 2017, 37(12):4302-4315. doi: 10.1002/joc.2017.37.issue-12
[28]	Smith M J, Goodchild M F, Longley P A. Geospatial Analysis: A Comprehensive Guide to Principles Techniques and Software Tools[M]. Sleaford: The Winchelsea Press, 2018.
[29]	Wieder W. Regridded Harmonized World Soil Database v1.2 [DB/OL]. (2014-09-15) [2021-03-05]. https://daac.ornl.gov/SOILS/guides/HWSD.html.
[30]	孔维尧, 李欣海, 邹红菲. 最大熵模型在物种分布预测中的优化[J]. 应用生态学报, 2019, 30(6):2116-2128.
	[ Kong W Y, Li X H, Zou H F. Optimizing MaxEnt model in the prediction of species distribution[J]. Chinese Journal of Applied Ecology, 2019, 30(6):2116-2128.]
[31]	International Food Policy Research Institute. Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 2.0[DB/OL].(2019-04-04) [2021-07-11]. https://cgiarcsi.community/2019/01/04/global-spatially-disaggregated-crop-production-statistics-data-for-2010/.
[32]	Xiao X M, Boles S, Frolking S, et al. Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images[J]. Remote Sensing of Environment, 2006, 100(1):95-113. doi: 10.1016/j.rse.2005.10.004
[33]	Zhang G L, Xiao X M, Dong J W, et al. Fingerprint of rice paddies in spatial-temporal dynamics of atmospheric methane concentration in monsoon Asia[J]. Nature Communications, 2020, DOI: 10.1038/s41467-019-14155-5. doi: 10.1038/s41467-019-14155-5
[34]	Scrucca L, Fop M, Murphy T B, et al. Mclust 5: Clustering, classification and density estimation using gaussian finite mixture models[J]. The R Journal, 2016, 8(1):289-317. doi: 10.32614/RJ-2016-021
[35]	The R Core Team. R: A Language and Environment for Statistical Computing[R/OL]. (2012-10-26) [2021-07-11]. https: //ishare.iask.sina.com.cn/f/35148866.html.
[36]	段居琦, 周广胜. 中国水稻潜在分布及其气候特征[J]. 生态学报, 2011, 31(22):6659-6668.
	[ Duan J Q, Zhou G S. Potential distribution of rice in China and its climate characteristics[J]. Acta Ecologica Sinica, 2011, 31(22):6659-6668.]
[37]	Poor E E, Scheick B K, Mullinax J. Multiscale consensus habitat modeling for landscape level conservation prioritization[J]. Scientific Reports, 2020, DOI: 10.1038/s41598-020-74716-3. doi: 10.1038/s41598-020-74716-3
[38]	Goudarzi F, Hemami M R, Malekian M, et al. Species versus within-species niches: A multi-modelling approach to assess range size of a spring-dwelling amphibian[J]. Scientific Reports, 2021, DOI: 10.1038/s41598-020-79783-0. doi: 10.1038/s41598-020-79783-0