不同水肥作用下西藏青稞生长动力学机制与模拟研究
|
摘要
【目的】构建西藏高原地区多因子作用下的青稞动态生长模型。【方法】于2016─2017年在西藏农牧学院农田水利试验场,监测了不同水分和氮素状态下的青稞动态生长过程,基于方差分析法研究了水分和氮素对青稞动态过程的交互作用,在此基础上构建了包括4个自由度、描述土壤水分和氮素对春青稞干物质形成以及累积过程函数关系,构建了多因子作用条件下的青稞动态关系模型,分别采用2016年和2017年的试验资料对模型进行了参数率定和验证。【结果】西藏高原地区青稞动态生长过程受到了多种因素的影响,青稞的干物质增量和累积量与土壤水分和氮素投入量表现出非线性响应关系;采用偏相关系数能够有效地反映青稞的产量与干物质指标关系。模拟不同水分和氮素条件下的青稞生长过程与实测结果相比,全生育内Nash-Sutcliffe系数平均值为0.873;5%的显著水平下,不存在系统性偏差。【结论】构建的青稞动态生长关系模型能够描述西藏地区气候和土壤条件对青稞生长动态过程影响机理,具有较高的模拟精度。
【Objective】The objective of this paper is to present a model to simulate the growth of highland barley under water and nitrogen stresses.【Method】Field experiments were conducted in 2016—2017 at Nyingchi in Tibetan Plateau; during the experiment, we measured the growth of the highland barley under different combinations of soil water and nitrogen stresses; their interactive effect on the growth was analyzed using the variance analysis method. A dynamic model, including four variables, was developed to simulate the crop growth and its associated dry-matter accumulation. The data in 2016 and 2017 were used to calibrate and validate the model, respectively.【Result】The physiological development of the highland barley was affected by a multitude of factors.The accumulating rate of the dry matter in the crop increased nonlinearly with soil water and nitrogen, and the impact of each factor can be represented by a partial correlation coefficient. The mean Nash-Sutcliffe coefficient between the simulated and the measured dry matter was 0.872, and there was no systematic deviation between the simulated and measured dry matter at significant level of 5%.【Conclusion】The simulated and observed dry matters were consistent. The physiological nature of the model means that it can accurately describe the growth of the highland barley in Tibetan Plateau.
【Objective】The objective of this paper is to present a model to simulate the growth of highland barley under water and nitrogen stresses.【Method】Field experiments were conducted in 2016—2017 at Nyingchi in Tibetan Plateau; during the experiment, we measured the growth of the highland barley under different combinations of soil water and nitrogen stresses; their interactive effect on the growth was analyzed using the variance analysis method. A dynamic model, including four variables, was developed to simulate the crop growth and its associated dry-matter accumulation. The data in 2016 and 2017 were used to calibrate and validate the model, respectively.【Result】The physiological development of the highland barley was affected by a multitude of factors.The accumulating rate of the dry matter in the crop increased nonlinearly with soil water and nitrogen, and the impact of each factor can be represented by a partial correlation coefficient. The mean Nash-Sutcliffe coefficient between the simulated and the measured dry matter was 0.872, and there was no systematic deviation between the simulated and measured dry matter at significant level of 5%.【Conclusion】The simulated and observed dry matters were consistent. The physiological nature of the model means that it can accurately describe the growth of the highland barley in Tibetan Plateau.
引文
[1]程卫国,卢文喜,安永凯.吉林省水稻水分生产函数模型的适应性研究[J].灌溉排水学报,2015,34(2):61-66.
[2]汪顺生,李欢欢,王康三,等.宽垄沟灌下冬小麦水分生产函数试验研究[J].排灌机械工程学报,2017,35(11):987-992.
[3]孔维萍,鱼生智,王海峰,等.调亏灌溉下酿酒葡萄耗水特性及水分生产函数研究[J].灌溉排水学报,2017,36(2):93-100
[4]柴江颖,崔远来,汪文超,等.鄱阳湖流域水稻水分生产函数模型试验研究[J].中国农村水利水电,2014(8):1-4.
[5]姜丽莉,张忠学,郑恩楠.不同氮肥条件下水稻产量和叶片荧光参数差异性分析[J].灌溉排水学报,2018,37(10):75-80.
[6]张恒嘉.几种大田作物水分-产量模型及其应用[J].中国生态农业学报,2009,17(5):997-1 001.
[7]王康,沈荣开,王富庆.冬小麦生长及根系吸氮的动态模拟研究[J].灌溉排水,2002,21(1):6-10.
[8]WANG K,ZHANG R,HIROSHI Y.Characterizing heterogeneous soil water flow and solute transport using information measures[J].Journal of Hydrology,2009,370(1):109-121.
[9]SAEBOM L,CHUNGYEONG Y,KWANGWOOK J,et al.Comparative evaluation of runoff and water quality using HSPF and SWMM[J].Water Science&Technology A Journal of the International Association on Water Pollution Research,2010,62(6):1 401-1 409.
[10]HANSENS,JENSEN H E,NIELSEN N E,et al.Simulation of nitrogen dynamics and biomass production in winter wheat using the Danish simulation model DAISY[J].Fertilizer Research,1991,27(2/3):245-259.
[11]张新民.有机菜花生产技术效率及其影响因素分析:基于农户微观层面随机前沿生产函数模型的实证研究[J].农业技术经济,2010(7):60-69.
[12]孙云岭,杨树青,刘德平,等.水肥互作对大豆产量及氮肥利用的影响[J].灌溉排水学报,2018,37(10):81-86..
[13]韩宇平,周雯晶,代小平,等.综合灌溉用水效率研究:以河北省成安县为例[J].灌溉排水学报,2017,36(8):89-94.
[14]胡洪静,吴鑫淼,齐成伟,等.节水压采区农业种植结构多目标优化研究:以衡水市为例[J].灌溉排水学报,2017,36(10):95-99.
[15]RIJTEMA P E,KROES J G.Some results of nitrogen simulations with the model ANIMO[J].Fertilizer Research,1991,27(2/3):189-198.
[16]王康,沈荣开,沈言俐,等.作物水分与氮素生产函数的实验研究[J].水科学进展,2002,13(3):308-312.
[17]MORGAN T H,BIERE A W,KANEMASU E T.A dynamic model of corn yield response to water[J].Water Resources Research,1980,16(1):59-64.
[18]程达芳.多效蒸发过程模拟:以最优法求解多效蒸发各个参数[J].高校化学工程学报,1989(2):84-93.
[19]廖翔,张合,李豪杰,等.认知不确定下动态试验误差条件抽样分析方法[J].北京理工大学学报,2018,38(5):469-475.
[20]尤天慧,樊治平.不确定性多属性决策中确定熵权的一种误差分析方法[J].系统工程,2003,21(1):102-105.
[21]宋德海,冯跃华,高志永.河南省三义寨灌区主要农作物需水量动态预报[J].灌溉排水学报,2016,35(11):103-106.
[22]吴灏,黄英,王杰,等.基于CROPWAT模型的昆明市水稻需水量及灌溉用水量研究[J].灌溉排水学报,2015,34(7):101-104.
[23]李毅,周牡丹.新疆地区棉花和甜菜需水量的统计降尺度模型预测[J].农业工程学报,2014,30(22):70-79.
[24]邱金亮,王静,张连根,等.滴灌条件下冬马铃薯需水规律及作物系数试验研究[J].灌溉排水学报,2013,32(2):122-124.
[25]HUANG K,TORIDE N,GENUCHTEN M T V.Experimental investigation of solute transport in large,homogeneous and heterogeneous,saturated soil columns[J].Transport in Porous Media,1995,18(3):283-302.
[26]SHRESTHA S,MANANDHAR B.Evaluation of the root zone water quality model(RZWQM)using field-measured data from the tropical zone,Thailand[J].Water Air&Soil Pollution,2014,225(6):1 958-1 968.
[2]汪顺生,李欢欢,王康三,等.宽垄沟灌下冬小麦水分生产函数试验研究[J].排灌机械工程学报,2017,35(11):987-992.
[3]孔维萍,鱼生智,王海峰,等.调亏灌溉下酿酒葡萄耗水特性及水分生产函数研究[J].灌溉排水学报,2017,36(2):93-100
[4]柴江颖,崔远来,汪文超,等.鄱阳湖流域水稻水分生产函数模型试验研究[J].中国农村水利水电,2014(8):1-4.
[5]姜丽莉,张忠学,郑恩楠.不同氮肥条件下水稻产量和叶片荧光参数差异性分析[J].灌溉排水学报,2018,37(10):75-80.
[6]张恒嘉.几种大田作物水分-产量模型及其应用[J].中国生态农业学报,2009,17(5):997-1 001.
[7]王康,沈荣开,王富庆.冬小麦生长及根系吸氮的动态模拟研究[J].灌溉排水,2002,21(1):6-10.
[8]WANG K,ZHANG R,HIROSHI Y.Characterizing heterogeneous soil water flow and solute transport using information measures[J].Journal of Hydrology,2009,370(1):109-121.
[9]SAEBOM L,CHUNGYEONG Y,KWANGWOOK J,et al.Comparative evaluation of runoff and water quality using HSPF and SWMM[J].Water Science&Technology A Journal of the International Association on Water Pollution Research,2010,62(6):1 401-1 409.
[10]HANSENS,JENSEN H E,NIELSEN N E,et al.Simulation of nitrogen dynamics and biomass production in winter wheat using the Danish simulation model DAISY[J].Fertilizer Research,1991,27(2/3):245-259.
[11]张新民.有机菜花生产技术效率及其影响因素分析:基于农户微观层面随机前沿生产函数模型的实证研究[J].农业技术经济,2010(7):60-69.
[12]孙云岭,杨树青,刘德平,等.水肥互作对大豆产量及氮肥利用的影响[J].灌溉排水学报,2018,37(10):81-86..
[13]韩宇平,周雯晶,代小平,等.综合灌溉用水效率研究:以河北省成安县为例[J].灌溉排水学报,2017,36(8):89-94.
[14]胡洪静,吴鑫淼,齐成伟,等.节水压采区农业种植结构多目标优化研究:以衡水市为例[J].灌溉排水学报,2017,36(10):95-99.
[15]RIJTEMA P E,KROES J G.Some results of nitrogen simulations with the model ANIMO[J].Fertilizer Research,1991,27(2/3):189-198.
[16]王康,沈荣开,沈言俐,等.作物水分与氮素生产函数的实验研究[J].水科学进展,2002,13(3):308-312.
[17]MORGAN T H,BIERE A W,KANEMASU E T.A dynamic model of corn yield response to water[J].Water Resources Research,1980,16(1):59-64.
[18]程达芳.多效蒸发过程模拟:以最优法求解多效蒸发各个参数[J].高校化学工程学报,1989(2):84-93.
[19]廖翔,张合,李豪杰,等.认知不确定下动态试验误差条件抽样分析方法[J].北京理工大学学报,2018,38(5):469-475.
[20]尤天慧,樊治平.不确定性多属性决策中确定熵权的一种误差分析方法[J].系统工程,2003,21(1):102-105.
[21]宋德海,冯跃华,高志永.河南省三义寨灌区主要农作物需水量动态预报[J].灌溉排水学报,2016,35(11):103-106.
[22]吴灏,黄英,王杰,等.基于CROPWAT模型的昆明市水稻需水量及灌溉用水量研究[J].灌溉排水学报,2015,34(7):101-104.
[23]李毅,周牡丹.新疆地区棉花和甜菜需水量的统计降尺度模型预测[J].农业工程学报,2014,30(22):70-79.
[24]邱金亮,王静,张连根,等.滴灌条件下冬马铃薯需水规律及作物系数试验研究[J].灌溉排水学报,2013,32(2):122-124.
[25]HUANG K,TORIDE N,GENUCHTEN M T V.Experimental investigation of solute transport in large,homogeneous and heterogeneous,saturated soil columns[J].Transport in Porous Media,1995,18(3):283-302.
[26]SHRESTHA S,MANANDHAR B.Evaluation of the root zone water quality model(RZWQM)using field-measured data from the tropical zone,Thailand[J].Water Air&Soil Pollution,2014,225(6):1 958-1 968.