KMS XINJIANG INSTITUTE OF ECOLOGY AND GEOGRAPHY，CAS
| 基于根区水质模型的灌溉决策支持系统开发与应用评估 | |
| Alternative Title | Development and Application Evaluation of a Descision Support System for Irrigation Scheduling based on RZWQM2 |
| 陈小平 | |
| Subtype | 博士 |
| Thesis Advisor | 齐志明 ; 曾凡江 |
| 2020-06-30 | |
| Degree Grantor | 中国科学院大学 |
| Place of Conferral | 北京 |
| Degree Discipline | 理学博士 |
| Keyword | 根区水质模型 灌溉决策支持系统 水分生产率 光合特征 气候变化 RZWQM2 DSSIS Water Productivity Photosynthesis Climate Change |
| Abstract | 合理的灌溉制度能够保证作物产量并节约灌溉用水。因此， 精准及时的灌溉制度以满足作物需水量对于提高灌溉作物用水效率至关重要。 但是目前根据经验的漫灌控制方式过于粗放，而基于土壤水分传感器的灌溉控制方式必须要在田间布设土壤水分监测设备，不利于耕作，而且土壤水分传感器只能反映土壤水分状况， 而非作物本身所受的水分胁迫状况。 本研究利用根区水质模型（Root ZoonWater Quilty Model， RZWQM）模拟的作物水分胁迫系数开发并测试了一套全新的灌溉决策支持系统（DSSIS）；基于 3 年野外田间试验，从棉花产量和水分生产率上评估 DSSIS； 以策勒绿洲棉田为对象， 探讨不同灌溉控制方式和灌溉量对棉花光合作用和生长的影响； 以 1970–2000 为基准期，模拟按照当前农业管理措施未来气候变化对棉花产量和需水量的影响。 研究结果表明：1） DSSIS 相比基于土壤水分传感器（SMS） 和基于经验的灌溉控制方式，水分生产率（WP） 显著提高了 26％和 66％（p <0.05）。 充分和亏缺灌溉（75%的充分灌溉）下的 WP 差异不显著。此外，与基于 SMS 和经验灌溉控制方式相比， DSSIS 在经济收益上具有优势。 因此， DSSIS 在干旱区农田灌溉管理中是有前景的灌溉系统。2） 与基于 SMS 相比， DSSIS 显著增加了籽棉产量[1.05 Mg ha–1（32％） ]并提高了水分生产率[0.16 kg m–3（20％） ]。相对于亏缺灌溉，充分灌溉显著提高了棉花产量[0.69 Mgha–1（20％） ]，但水分生产率没有显著差异。总的来说，DSSIS 充分灌溉具有最高棉籽产量（4.55 Mg ha–1）和净收入（16855 元 ha–1 y–1），但 DSSIS 亏缺灌溉有着最高的水分生产率（1.01 kg m–3）。相比 DSSIS 充分和 SMS充分灌溉处理， DSSIS 亏缺灌溉用水量分别显著减少了 51 mm（10％）和 23 mm（5％）。因此， 在干旱气候下， 基于 DSSIS 亏缺灌溉可以维持棉花产量并提高水分生产率。3） 相比基于 SMS，基于 DSSIS 和基于经验的灌溉控制方式分别使叶片净光合速率（Pn） 显著提高了 20％和 20％，气孔导度（Gs） 显著增加了 39％和 37％，蒸腾速率（ Tr） 显著降低了 20％和 17％。 相比基于 SMS，基于 DSSIS 和基于经验的灌溉控制方式在棉花株高，叶面积指数，地上和根系生物量方面具有优势。此外，基于 DSSIS 和基于经验的灌溉控制方式单株有效棉铃重量和数量显著高于基于 SMS。 相比充分灌溉，亏缺灌溉导致 Pn， Gs 和 Tr 分别下降了 11％， 19％和 10％， 但对单株果枝和铃重没有显著影响。 因此，基于 DSSIS 可以在干旱条件下维持棉花的光合作用和生长发育。4） 对于研究区域， 6 种全球气候模式下分别预测的 2041-2060 和 2061-2080生长季温度升高 2.38°C 和 3.24°C，降水量增加 3.5％和 5.3％。 相比基准期， 预计 2041–2060 年， 中排放情景（RCP4.5）下籽棉产量将增加 0.24 Mg ha–（1 5.6％），高排放情景（RCP8.5） 下籽棉产量将增加 0.19 Mg ha–1（4.5％）。但是，对于2061–2080， 在 RCP4.5 下预测的产量增加 0.32 Mg ha–1（7.6％），而在 RCP8.5下产量减少 0.28 Mg ha–1（6.5％）。棉花单产增加主要归因于增加的 CO2 施肥效应超过增温导致生育期缩短（8.0-9.5 天）的不利影响。 得益于增加的 CO2 和缩短的生育期， 相比基准期（786mm）， 在 RCP4.5 和 RCP8.5 情景中， 分别模拟2041–2060 和 2061–2080 生长季棉花平均需水量为 728 mm 和 706 mm，降低了7.5％和 10.3％。基于RZWQM2模拟作物水分胁迫的DSSIS灌溉控制方式能够有效地指导干旱区田间灌溉管理；权衡作物产量、 耗水量以及水分生产率，推荐区域灌溉制度采用 DSSIS 亏缺灌溉； DSSIS 灌溉控制方式对棉花的光合作用与生长发育没有显著性影响，特别是在花期和铃期；按照当前的管理措施， 未来该地区的棉花产量将增加并且需水量减少。 |
| Other Abstract | A reasonable irrigation system can ensure crop yield and save irrigation water.Therefore, an accurate and timely irrigation schedule to meet crop water requirementis vital to improving water use efficiency. However, the current experience–basedirrigation control method is too extensive, and soil moisture sensor (SMS)–basedirrigation control method must be equipped with soil moisture monitoring equipmentin the field, which is not conducive to farming. In addition, SMS can only reflect thesoil moisture conditions, rather than the crop water stress conditions. In this study, aDecision Support System for Irrigation Scheduling (DSSIS), based on water stressfactor simulated by Root Zoon Water Quilty Model (RZWQM), was developed andtested; The DSSIS was evaluated based on 3–year field experiments from cotton yieldand water productivity; Taking the cotton field in Qira Oasis as an object, the effectsof different irrigation control methods on photosynthesis and growth of cotton werediscussed; Using 1970–2000 as the baseline, the impacts of climate change on cottonyield and water requirement were simulated by RZWQM2 under current managementpractices. The main results are listed as follows:1）The DSSIS significantly increased water productivity (WP) by 26% and 65.7%,compared to SMS–based and experience–based irrigation control methods (p<0.05),respectively. No significant difference was observed in WP between full and deficitirrigation (75% of full irrigation) treatments. In addition, the DSSIS showed economicadvantage over SMS– and experience–based methods. Our results suggested thatDSSIS is a promising irrigation system in farmland irrigation management in aridregion.2 ） Implementation of the DSSIS–based irrigation control method led tosignificant increases in seed cotton yield [1.05 Mg ha–1(32%)] and WP [0.16 kg m–3(20%)] compared to the SMS–based irrigation control method. Full (vs. deficit)irrigation significantly increased cotton yield [0.69 Mg ha–1 (20%)] but had nosignificant effect on water productivity. In general, while the DSSIS–based under fullirrigation (DSS–FI) treatment provided the greatest cotton seed yield (4.55 Mg ha–1)and net income (¥16855 ha–1), the highest WP (1.01 kg m–3) was achieved under theDSSIS–based under deficit irrigation (DSS–DI) treatment. Water use under DSS–DI treatment significantly decreased by 51 mm (10%) and 23 mm (5%), respectively,compared to DSS–FI and SMS–based under full irrigation (SMS–FI) treatments.Therefore, our results demonstrated that the DSSIS–based irrigation control methodwith deficit irrigation could maintain cotton yield and improve WP in an arid climate.3） Compared to SMS–based irrigation control method, DSSIS–based andexperience–based irrigation control method significantly enhanced the leaf netphotosynthetic rate (Pn) by 20% and 20%, stomatal conductance (Gs) by 39% and37%, transpiration rate (Tr) by 20% and 17%, respectively. DSSIS–based andexperience–based irrigation control methods have an advantage in plant height, leafarea index, aboveground and root biomass of cotton compared to sensor–basedirrigation control method. Moreover, the weight and number of effective bolls perplant for DSSIS–based and experience–based irrigation methods were significantlyhigher than sensor–based irrigation control method. Deficit irrigation, causing a 11%,19%, and 10% decline in Pn, Gs, and Tr, respectively, did not negatively affectfruiting branches and weight of per boll, compared to full irrigation. It is concludedthat DSSIS–based irrigation control method can maintain leaf photosynthesis andgrowth of cotton under an arid condition.4） For the study region, six general circulation models (GCMs) predicted anincrease of 2.38°C and 3.24°C in temperature and 3.5% and 5.3% in precipitationduring the growing seasons for 2041–2060 and 2061–2080, respectively. For2041–2060, seed cotton yield was projected to increase by 0.24 Mg ha–1 (5.6%) undermoderate representative concentration pathway (RCP4.5) scenario and 0.19 Mg ha–1(4.5%) under high representative concentration pathway (RCP8.5) scenario; however,for 2061–2080, the model predicted a 0.32Mg ha–1 (7.6%) yield increase underRCP4.5 but a 0.28 Mg ha–1 (6.5%) decrease under RCP8.5. The increased cotton yieldwas mainly attributable to the fertilization effect of elevated CO2 (eCO2) dominatingthe detrimental effects of shorter growing seasons (8.0–9.5 days). Alleviated lowtemperature stress also slightly promoted cotton yield. Averaged across the RCP4.5and RCP8.5 scenarios, simulated cropping season water requirement for the2041–2060 and 2061–2080 were 728 mm and 706 mm, respectively, a decrease by 7.5%and 10.3% relative to the present-day baseline (786 mm), respectively. This decreasewas attributed to shorter growing seasons and eCO2.DSSIS irrigation control method based on crop water stress simulated by RZWQM2 can effectively guide field irrigation management in arid region. DSSISdeficit irrigation was recommended for regional irrigation scheduling in terms of cropyield, water consumption, and water productivity. DSSIS irrigation control methodhas no significant effect on photosynthesis and growth of cotton, especially atflowering and boll stages. An increase in cotton yield and decrease in waterrequirement were found under current management methos in the future. |
| Subject Area | 生态学 |
| Language | 中文 |
| Document Type | 学位论文 |
| Identifier | http://ir.xjlas.org/handle/365004/15410 |
| Collection | 中国科学院新疆生态与地理研究所 研究系统 |
| Affiliation | 中国科学院新疆生态与地理研究所 |
| First Author Affilication | 中国科学院新疆生态与地理研究所 |
| Recommended Citation GB/T 7714 | 陈小平. 基于根区水质模型的灌溉决策支持系统开发与应用评估[D]. 北京. 中国科学院大学,2020. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment