施氮和灌溉管理下作物产量和土壤生化性质

Crop yield and soil biochemical properties under different nitrogenfertilization and irrigation management schemes

  • 摘要: 氮和水对作物生长非常重要, 研究施氮和灌溉管理下作物产量、土壤性质以及它们的关系对我国农业科学有重要意义。本文以中国科学院封丘农业生态试验站为平台, 研究了施氮(每季施氮150 kg·hm-2、190 kg·hm 2、230 kg·hm-2、270 kg·hm-2, 以不施氮为对照)和灌溉(灌溉量达到0~20 cm、0~40 cm、0~60 cm土壤的田间持水量, 以雨养为对照)管理下小麦?玉米轮作系统作物产量和土壤生化性质, 以及它们之间的关系。研究结果表明, 150~270 kg·hm-2施氮量对2008年、2009年玉米产量和2009年、2010年小麦产量无显著影响; 灌溉对2010年玉米产量无显著影响, 而2008年、2009年玉米产量随灌溉量增大而增加。尽管2008-2011年小麦产量随灌溉量变化趋势不一致, 但与雨养相比灌溉提升了小麦产量。施氮在不同程度上提升了土壤全氮和速效氮含量、脱氢酶和脲酶活性、微生物生物量碳、基本呼吸和硝化势, 稍微降低了土壤pH并大幅降低了速效磷含量(降幅48.7%~51.6%); 灌溉提升了土壤全氮含量和脱氢酶活性, 降低了全钾含量、脲酶活性、基本呼吸、硝化势。多元回归分析显示, 某些土壤生化性质(全氮、溶解性有机碳、速效磷含量及微生物生物量碳氮、呼吸熵、硝化势)与2009年、2010年玉米产量很好地线性拟合。综上, 土壤生化性质因施氮和灌溉发生不同程度的分异, 因施氮和灌溉而分异的土壤生化性质能部分地鉴定作物产量。本研究方法可为产量主导因子的筛选及产量估算模型的建立提供一定参考依据。

     

    Abstract: It is generally known that nitrogen (N) and water are critical for crop growth. It is therefore important to study the effects of N fertilization and irrigation on crop yield, soil properties and their relationship to crop yield. However, domestic studies have provided little details about the relationship between crop yield and soil properties influenced by N fertilization and irrigation management schemes. Foreign studies have mainly focused on the relationship between soil physicochemical properties and crop yield, and the relationship of crop yield with soil biochemical properties not well documented. To address this knowledge gap, this study explored the effects of N fertilization and irrigation management schemes on crop yield and soil biochemical properties and their relationship. N fertilization and irrigation management schemes were initiated in 2005 at the Fengqiu Agro-Ecological Experimental Station of Chinese Academy of Sciences. Under summer maize (Zea mays L.) and winter wheat (Triticumae stivum L.) crop rotation system, N fertilizer was applied at the rates of 150 kg·hm-2, 190 kg·hm-2, 230 kg·hm-2 and 270 kg·hm -2 per crop season and non-N input used as the control. Irrigation was done to meet soil field capacity of the 0 20 cm, 0 40 cm and 0 60 cm soil layers and also with rain-fed treatment as the control. Soil samples were collected at 0 20 cm soil depth in June 2011 and basal biochemical properties determined. Meanwhile, crop yield data for 2008 2011 were analyzed. The results showed that N fertilization rate of 150 270 kg·hm-2 did not significantly enhance maize yield in 2008 and 2009, and wheat yield in 2009 and 2010. Irrigation little influenced maize yield in 2010, while maize yield in 2008 and 2009 gradually increased with increasing irrigation amount. Compared with rain-fed system, irrigation increased wheat yield in 2008 2011. N fertilization increased soil total N content (TN), available N content (AN), dehydrogenase activity (DHD), urease activity (URE), microbial biomass carbon content (MBC), basal soil respiration (BSR) and nitrification potential (NP) to varying degrees. N fertilization slightly decreased soil pH and sharply decreased available P content (AP) by 48.7% 51.6%. Irrigation slightly increased TN and DHD and then decreased URE, BSR, NP and total K content (TK) to varying degrees. With the exception of TK, AN, and DHD, correlation analysis showed significant correlations among these properties. Principal component analysis was used to select highly weighted factors for explaining yield variation. The selected properties included TN, DOC, AP, MBC, MBN, NP and quotient of respiration (qCO2). After analyses using the determined biochemical properties, multi-collinearity problem was resolved. Finally, multiple linear regression analysis was performed using the selected properties (TN, DOC, AP, MBC, MBN, NP, qCO2) and crop yield, in which all the regression equations of predicted maize yield in 2009 and 2010 were highly significant. In conclusion, differentiation of soil biochemical properties resulting from N fertilization and irrigation partly estimated crop yield. This research method provided the needed reference for selecting yield determinants and building yield models.

     

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