陕西黄土区农田土壤主要养分特征及影响因素

doi:10.13866/j.azr.2023.12.04

干旱区研究 ›› 2023, Vol. 40 ›› Issue (12): 1907-1917.doi: 10.13866/j.azr.2023.12.04 cstr: 32277.14.j.azr.2023.12.04

陕西黄土区农田土壤主要养分特征及影响因素

贺军奇^1,^2,³(),拜寒伟^1,^2,³,徐轶玮⁴,倪莉莉⁵

1.长安大学水利与环境学院，陕西西安 710054
2.长安大学旱区地下水文与生态效应教育部重点实验室，陕西西安 710054
3.长安大学水利部旱区生态水文与水安全重点实验室，陕西西安 710054
4.陕西省科技交流中心，陕西西安 710054
5.陕西省耕地质量与环境保护工作站，陕西西安 710003

收稿日期:2023-06-06 修回日期:2023-09-11 出版日期:2023-12-15 发布日期:2023-12-18
作者简介:贺军奇（1978-)，男，副教授，主要从事干旱区农田水利研究. E-mail: hejunqi@chd.edu.cn
基金资助:
国家自然科学基金项目(41901034);国家自然科学基金项目(42001033);陕西省哲学社会科学研究专项(2022HZ1859)

Main nutrient characteristics and influencing factors of farmland soil in the Loess Plateau of the Shaanxi Province

HE Junqi^1,^2,³(),BAI Hanwei^1,^2,³,XU Yiwei⁴,NI Lili⁵

1. School of Water and Environmental, Chang’an University, Xi’an 710054, Shaanxi, China
2. Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of Ministry of Education, Chang’an University, Xi’an 710054, Shaanxi, China
3. Key Laboratory of Ecological Hydrology and Water Security in Arid Areas, Ministry of Water Resources, Chang’an University, Xi’an 710054, Shaanxi, China
4. Shaanxi Science and Technology Exchange Center, Xi’an 710054, Shaanxi, China
5. Shaanxi Province Farmland Quality and Agricultural Environmental Protection Workstation, Xi’an 710003, Shaanxi, China

Received:2023-06-06 Revised:2023-09-11 Published:2023-12-15 Online:2023-12-18

摘要/Abstract

摘要：

为了探究陕西黄土区农田主要土壤养分特征及其影响因素，基于研究区5096个农田0~20 cm耕层采样点数据，利用GIS与地统计学方法对有机质（SOM）、全氮（TN）、有效磷（AP）和速效钾（AK）含量进行空间分析，并应用地理探测器模型探究18种影响因素对养分空间变异的解释程度。结果表明：SOM、TN、AP和AK含量均值分别为14.43 g·kg^-1、0.92 g·kg^-1、18.21 mg·kg^-1和190.28 mg·kg^-1，呈现中等程度变异；4种养分最佳拟合模型均为指数模型，各养分呈现中等程度空间相关性，结构性与随机性因素的共同作用导致了养分含量的空间差异；养分全局空间相关性大小表现为：TN>SOM>AK>AP；养分含量区域差异明显，呈现由北向南逐渐递增的趋势；年日照时长、年均气温、化肥用量和地貌类型等单因子作用对各养分含量空间变异具有更强的解释力，两因子交互作用对养分的解释力强于其单因子解释力。研究表明，陕北地区宜适当增加肥料投入，关中地区宜进行精耕细作，农田建设应考虑多方面因素。

关键词: 土壤主要养分, 空间变异, 影响因素, 地理探测器模型, 陕西黄土区

Abstract:

To explore the main soil nutrient characteristics and influencing factors of farmland in the Loess Plateau of Shaanxi, spatial analysis was conducted on the content of SOM, TN, AP, and AK using GIS and geostatistics methods based on the data from 5096 farmland sampling points with a depth of 0-20 cm in the study area. A geographic detector model was used to explore the degree to which 18 influencing factors explained the spatial variation of nutrients. The results showed that the average contents of SOM, TN, AP, and AK were 14.43 g·kg^?1, 0.92 g·kg^?1, 18.21 mg·kg^?1, and 190.28 mg·kg^?1, respectively, showing moderate variation. The four best-fit models for nutrients were all exponential models, with moderate spatial correlation among each nutrient. The combined effect of structural and random factors causes spatial differences in the nutrient content. The global spatial correlation of nutrients is TN > SOM > AK > AP. The regional differences in nutrient content are significant, indicating a gradually increasing trend from north to south. The single-factor effects of annual sunshine duration, annual average temperature, fertilizer use, and geomorphic type have stronger explanatory power for spatial variation in nutrient content but lesser than that of the interaction between the two factors. Research has shown that it is necessary to increase fertilizer input in the northern Shaanxi region, perform intensive cultivation in the Guanzhong region, and consider multiple factors in farmland construction.

Key words: main nutrients in soil, spatial variation, influencing factors, geological detector model, the Loess Plateau of Shaanxi Province

贺军奇,拜寒伟,徐轶玮,倪莉莉. 陕西黄土区农田土壤主要养分特征及影响因素[J]. 干旱区研究, 2023, 40(12): 1907-1917.

HE Junqi,BAI Hanwei,XU Yiwei,NI Lili. Main nutrient characteristics and influencing factors of farmland soil in the Loess Plateau of the Shaanxi Province[J]. Arid Zone Research, 2023, 40(12): 1907-1917.

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类型	变量	影响因子	单位/分类
土壤属性	pH	X1	-
	土壤容重	X2	g·cm^-3
	土壤类型	X3	潮土、风沙土、黄绵土、新积土、褐土、黑垆土、红黏土、水稻土
	成土母质	X4	黄土母质、风沙母质、冲积母质、湖积母质、残积母质、坡积母质、红土母质
	土壤质地	X5	砂土、砂壤土、轻壤土、中壤土、重壤土、黏壤土、黏土
气候因素	年日照时长	X6	h
	年降雨量	X7	mm
	年平均气温	X8	℃
	年蒸发量	X9	mm
地形因素	高程	X10	m
	坡度	X11	°
	坡向	X12	阳坡（0°~45°、315°~360°）、半阳坡（45°~90°、270°~315°）、半阴坡（90°~135°、225°~270°）、阴坡（135°~225°）（以正北方向为基准）
	地表起伏度	X13	m
	地貌类型	X14	长城沿线风沙滩区、陕北黄土丘陵沟壑区、渭北黄土台塬区、关中平原区
人为活动	化肥用量	X15	kg·hm^-2
	灌溉方式	X16	无灌溉、漫灌、畦灌、滴灌、沟灌、喷灌
	作物类型	X17	谷类作物、果蔬作物、薯类作物、药用作物、豆类作物、油料作物、其他
	熟制	X18	一年一熟、一年两熟、一年三熟、两年三熟、多年生

判断依据	交互作用
q（X₁∩X₂）>q（X₁）+q（X₂）	非线性增强
q（X₁）+q（X₂）>q（X₁∩X₂）>max[q（X₁），q（X₂）]	双因子增强
q（X₁∩X₂）=q（X₁）+q（X₂）	相互独立
max[q（X₁），q（X₂）]>q（X₁∩X₂）>min[q（X₁），q（X₂）]	单因子非线性减弱
q（X₁∩X₂）<min[q（X₁），q（X₂）]	非线性拮抗

指标	样本数	最大值	最小值	平均值	标准差	变异系数/%	分布类型	偏度	峰度
SOM/（g·kg^-1）	5079	32.97	1.26	14.43	6.10	42.27	正态分布	-0.10	2.52
TN/（g·kg^-1）	5084	2.10	0.04	0.92	0.39	42.39	正态分布	-0.18	2.50
AP/（mg·kg^-1）	4995	71.28	0.04	18.21	14.55	79.90	对数正态	-0.42	3.50
AK/（mg·kg^-1）	5020	550.00	18.00	190.28	108.24	56.88	对数正态	0.04	2.56

指标	理论模型	块金值C₀	基台值C₀+C	块金系数C₀/(C₀+C)	变程A/km	决定系数R²	残差平方和RSS	全局Moran’s I	Z
SOM	指数模型	0.1941	0.3892	0.4987	40.8	0.979	3.79×10^-4	0.58	86.31
TN	指数模型	0.0134	0.0231	0.5931	58.9	0.995	3.31×10^-6	0.61	90.90
AP	指数模型	0.3530	0.7070	0.4993	92.4	0.994	5.56×10^-4	0.31	44.79
AK	指数模型	0.1573	0.2299	0.6842	67.3	0.964	2.46×10^-4	0.36	53.40

交互作用	SOM		TN		AP		AK
交互作用	q	交互类型	q	交互类型	q	交互类型	q	交互类型
交互作用1	X1∩X6 0.496	双因子增强	X1∩X6 0.535	双因子增强	X8∩X15 0.229	双因子增强	X8∩X15 0.303	双因子增强
交互作用2	X14∩X15 0.492	双因子增强	X6∩X15 0.512	双因子增强	X10∩X14 0.227	双因子增强	X10∩X15 0.299	双因子增强
交互作用3	X6∩X18 0.487	双因子增强	X2∩X6 0.506	双因子增强	X1∩X8 0.226	双因子增强	X6∩X10 0.298	双因子增强
交互作用4	X1∩X12 0.479	双因子增强	X5∩X6 0.505	双因子增强	X1∩X10 0.225	双因子增强	X7∩X8 0.295	双因子增强
交互作用5	X1∩X15 0.478	双因子增强	X6∩X9 0.498	双因子增强	X15∩X16 0.222	双因子增强	X6∩X8 0.294	双因子增强

陕西黄土区农田土壤主要养分特征及影响因素

Main nutrient characteristics and influencing factors of farmland soil in the Loess Plateau of the Shaanxi Province

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