基于夏玉米产量和氮素利用的水氮减量方案优选
2023-03-23王旭敏刘朋召李巧丽王小利
李 慧 王旭敏 刘 苗 刘朋召 李巧丽 王小利 王 瑞 李 军
基于夏玉米产量和氮素利用的水氮减量方案优选
李 慧 王旭敏 刘 苗 刘朋召 李巧丽 王小利 王 瑞 李 军*
1西北农林科技大学农学院, 陕西杨凌 712100;2农业农村部西北黄土高原作物生理生态与耕作重点实验室, 陕西杨凌 712100
针对当前夏玉米生产中水氮投入不合理, 缺少综合夏玉米产量、氮素利用及土壤硝态氮含量对水氮优化管理模式评价的问题, 运用层次分析法、熵权法、博弈论组合赋权计算各指标权重, 使用TOPSIS法建立模型对水氮减量方案进行综合评价, 为关中平原夏玉米节水节肥环保增效的生产模式提供理论依据。于2018—2020年在陕西杨凌开展水、氮二因素裂区田间试验。设置3个灌溉处理, 以传统灌水量(800 m3hm–2, W2)为对照、在此基础上减50% (400 m3hm–2, W1)和减100% (0 m3hm–2, W0)。每个灌溉量下设5个施氮梯度, 以传统施氮量(300 kg hm–2, N300)为对照、在此基础上减25% (225kg hm–2, N225)、减50% (150 kg hm–2, N150)、减75% (75 kg hm–2, N75)和减100% (0)。分析不同水氮减量处理夏玉米产量、氮素利用及土壤硝态氮含量, 使用TOPSIS法建模选优。与对照W2N300相比, W1N225增产效果最明显, 增产率为5.4%, W2N225、W2N150、W1N150也表现出明显的增产效应, 增产率分别为2.4%、0.7%、0.3%。W1N225、W1N150可以显著提高氮肥农学效率、氮肥回收效率、氮肥偏生产力, 2018年NAE、NRE、NPFP分别比传统模式提高29.7%、16.2%、24.5%, 36.5%、25.4%、28.8%; 2019年分别提高53.4%、36.7%、32.8%, 46.5%、35.2%、47.4%; 2020年分别提高43.6%、37.3%、48.0%, 66.9%、43.1%、54.5%。W1N225、W1N150土壤硝态氮残留量比传统水氮管理模式减少28.6%、53.8%。使用TOPSIS法进行综合评价, 发现氮肥减量25%~50%、灌水减少50%时各指标评价值最高, 水氮减量(中水中肥)优于高水高肥, 高水高肥优于低水低肥, 高水低肥优于低水高肥。通过TOPSIS法模拟寻优得出灌水量为W1 (400 m3hm–2)施氮量为200 kg hm–2时综合评价值最优。因此, 在关中平原灌溉区, 灌水减量50% (400 m3hm–2)、施氮减少33.3% (200 kg hm–2)可以实现关中平原夏玉米生产节水减肥环保增效的目标。
夏玉米; 水氮减量; 氮素利用; 产量; TOPSIS法
施氮和灌溉是促进作物生长发育和产量提高的重要途径[1]。我国纯氮施用量在逐年上升, 平均每年增加约7.8×105t[2], 关中地区农田平均施氮量高达(288±113) kg hm–2 [3], 过量氮肥投入导致氮肥利用效率降低, 使得我国农作物氮肥利用率平均只有28%~41%, 远低于40%~60%的世界平均水平[4-5]。我国农业水资源供需矛盾日益明显, 关中地区灌溉水资源短缺严重[6], 节约农业灌溉用水也是亟待解决的重要问题。目前不合理的农业生产方式, 不仅降低了水肥利用效率, 而且引发了环境污染等一系列问题[7-8]。解决玉米栽培中水氮过量投入、资源浪费突出的问题, 有助于实现我国农业从以“增产为核心”的单一目标向“可持续发展的高产高效、环境友好”多重目标转变[9]。
农田水氮存在明显的交互效应[10], 水氮互作可显著提高玉米产量, 其中氮为主效[11], 科学的水肥管理可以有效促进水氮耦合效应[12]。前人研究表明, 减氮条件下适量滴灌有利于提高氮肥吸收利用率, 充分发挥水氮协同和耦合效应, 弥补减氮带来的产量损失, 川中丘陵地区纯氮180 kg hm–2、滴灌750~1125 m3hm–2[13]有利于促进玉米生长, 提高产量和氮素利用。在关中平原, 与常规施氮300 kg hm–2、灌溉800 m3hm–2相比, 适宜减少施氮和灌水不会造成产量减少[14]。控释氮肥减量25%可使玉米小幅度增产, 且氮素利用效率显著提高[15]。黄土高原旱地玉米连作农田单施氮肥180 kg hm–2处理下, 23年后0~300 cm土层硝态氮残留量高达1500 kg hm–2 [16]。渭北旱地春玉米施氮量高于180 kg hm–2, 0~200 cm土层硝态氮残留量高达504.7~620.8 kg hm–2, 存在氮素淋溶风险[17]。
在传统水肥管理模式基础上适宜的水氮减量, 可以有效提高玉米水肥资源利用效率, 减少硝态氮淋溶风险[7]。前人已经在灌水和施氮单一因素对玉米产量、氮素利用和硝态氮积累等方面开展了相关研究[18-22], 但相关研究多为单因素回归分析, 适用于指标较少的情况, 不利于多指标判定, 夏玉米产量、氮素利用、土壤硝态氮含量各指标衡量标准不尽相同, 但却相互影响。因此本研究通过3年夏玉米田间定位灌溉和施氮试验, 分析水氮共同减量处理下水氮互作对玉米产量、氮素利用和土壤硝态氮的影响, 运用层次分析法[23]、熵权法[24]、博弈论组合赋权[25]得到各指标综合权重, 并通过TOPSIS法[26]建模综合选优, 旨在达到水氮量化管理, 以期为关中平原夏玉米发展高产、高效、节水、节肥、环保的生产模式提供科学依据。
1 材料与方法
1.1 试验区概况
于2018—2020年在陕西省杨凌区西北农林科技大学曹新庄试验农场(34°20′N, 108°07′E)实施。当地属于大陆性季风暖温带半湿润气候, 年均日照时数2163.8 h, 年均气温12.9℃, 年均降水量635.1 mm, 年均蒸发量993.2 mm, 无霜期211 d, 供试土壤为壤土。试验地种植制度为冬小麦、夏玉米一年二熟制轮作, 试验开始前土壤养分见表1。2018—2020年夏玉米均为6月14日播种, 2018年为10月1日收获, 2019年和2020年为9月30日收获, 生育期内降水量分别为335.3、499.8和508.9 mm, 2018年和2020年生育前期降水较密集, 2019年生育后期降水较密集(图1)。
表1 试验地0~60 cm土层基础理化性状
图1 夏玉米生长季月降雨量
1.2 试验设计
采用二因素裂区设计, 灌溉量为主区, 施氮量为副区。灌溉量设3个灌溉水平: 传统灌水量(800 m3hm–2, W2)为对照, 于拔节期和抽雄期各灌溉400 m3hm–2; 减量50% (400 m3hm–2, W1), 于拔节期进行; 不灌溉(W0)。施氮量设5个施氮水平: 传统施氮量(300 kg hm–2, N300)为对照、减施25% (225 kg hm–2, N225)、减施50% (150 kg hm–2, N150)、减施75% (75 kg hm–2, N75)和不施氮(N0)。小区面积91 m2(6.5 m×14.0 m), 3次重复, 共计45个小区。施用氮肥为尿素, 试验地统一施磷肥为P2O5120 kg hm–2,本地区农田土壤富含钾素, 因此本试验不施钾肥, 氮、磷肥全部基施。供试玉米品种为郑单958, 密度6×104株 hm–2。灌溉方式采用喷灌, 水表控制灌水量, 其他管理措施同当地生产习惯。
1.3 测定项目与方法
1.3.1 产量及产量构成因素 在玉米成熟期, 每个小区选取行长5 m长势均匀的玉米各3行, 统计穗数, 在行内选取20个均匀果穗, 取3次重复, 风干后于室内考种, 测定穗粒数和百粒重, 按14%含水量折算籽粒产量。
1.3.2 植株样品 于夏玉米成熟期, 采集不同处理具有代表性玉米植株3株, 取其地上部分105℃杀青30 min, 85℃烘干至恒重后称重并粉碎, 采用H2SO4-H2O2消煮, 凯氏定氮法测定全氮含量[27]。
1.3.3 土壤硝态氮 在2018和2019年夏玉米成熟期, 用土钻采集0~200 cm、2020年采集0~300 cm土层土样, 每20 cm为一个土层。将待测土样过2 mm筛, 称取5.0 g鲜土样, 用50 mL 1 mol L–1KCl溶液浸提, 振荡1 h后过滤, 用连续流动分析仪(AA3型)测定硝态氮含量。
1.3.4 权重确定 由于产量、氮素利用和土壤硝态氮含量评价指标各不相同, 相互之间无法直接比较, 为消除量纲不同的影响, 先对数据进行归一化处理, 再利用不同方法确定各指标所占权重, 运用层次分析法[20]进行主观层次分析, 利用熵权法[21]进行客观权重分析, 由于二者之间存在差异, 运用博弈论组合赋权[22]获得最终权重。
1.3.5 测定项目相关计算方法[16]收获指数 = 籽粒产量/地上部生物量; 氮肥农学效率(NAE, kg kg–1) = (施氮区玉米产量–不施氮区玉米产量)/施氮量; 氮肥回收效率 (NRE, %) = (施氮区植株地上部氮积累量–不施氮区植株地上部氮积累量)/施氮量 × 100; 氮肥偏生产力(NPFP, kg kg–1) = 施氮区籽粒产量/施氮量; 每一土层硝态氮残留量(NR, kg hm–2) = 土壤硝态氮含量(mg kg–1) ×土层厚度(cm) × 土壤容重(g cm–1)/10。一定深度土壤硝态氮残留总量为各个土层硝态氮残留量之和。
1.4 数据分析
使用SPSS19.0分析试验数据, 采用Duncan’s法多重比较, 差异显著性水平<0.05。使用Origin 2018、2021制图。
2 结果与分析
2.1 不同水氮减量处理对夏玉米产量的影响
由表2可知, 3年试验期间, 除2019年外, 施氮量和灌水量及其互作对夏玉米产量有显著影响。试验结果表明, 在同一灌水水平下, 随施氮量的减少, 产量总体呈现先增后降的趋势, N225优于其他处理。在同一施氮水平下, W1和W2产量差异不显著, 但都与W0差异显著, 与W2相比, 3年平均产量W0减少13.6%。在不同水氮处理下, 与W2N300相比, 2018年W2N225产量最高, 增高2.1%, 2019、2020年W1N225与W1N300产量最高, 分别增高5.0%、5.5%, 10.5%、8.8%。连续3年田间试验显示, 与对照W2N300相比, W2N225、W2N150、W1N300、W1N225、W1N150增产2.4%、0.7%、4.5%、5.4%、0.3%, 其中W1N225增产最高。
2.2 不同水氮减量处理对夏玉米氮素吸收利用的影响
由表3可知, 施氮量和灌水量及其互作对夏玉米地上部吸氮量有极显著影响。总体来说, 在同一灌水水平下, 随施氮量的减少, 2018年和2019年氮素吸收呈减小趋势, 2020年则是先增后减, 但N225和N300无显著差异。由此说明, 植株吸氮量存在阈值, 超过阈值继续增施氮肥不会改善作物生长潜力。在同一施氮水平下, 植株吸氮量随灌水量的减少而减少, 与W2相比, 2018年和2019年植株吸氮量W1和W0减少6.8%、21.0%和12.1%、17.8%, 2020年W1与W2则无显著差异, 由此说明, 同一施氮下W2需要更多的氮肥供应。施氮和灌水对氮肥农学效率、氮肥回收率、氮肥偏生产力具有显著影响。在同一灌水水平下, 随施氮量的减少, NAE、NRE、NPFP都呈增加趋势。在同一施氮量下, NAE随灌水量的减少呈先增后降的趋势, 与W2相比, 2018、2019、2020年W1分别增大6.3%、14.1%、17.7%。2018、2020年NRE随灌水量的减少呈先增后降, 2019年则是随施氮量的减少呈现先降后增的趋势。2018、2019、2020年NPFP在W1、W2无显著差异, 与W0差异显著。不同水氮处理下, 与W2N300相比, W2N225、W2N150、W1N225、W1N150在2018年NAE、NRE、NPFP分别提高30.8%、19.9%、26.5%, 26.3%、24.3%、25.5%, 40.7%、26.4%、28.2%, 40.8%、30.5%、49.5%; 2019年分别提高45.4%、49.7%、48.6%, 63.0%、32.4%、52.8%, 29.7%、16.2%、24.5%, 36.5%、25.4%、28.8%, 2020年分别提高53.4%、36.7%、32.8%, 46.5%、35.2%、47.4%, 43.6%、37.3%、48.0%和66.9%、43.1%、54.5%。
表2 不同水氮减量处理下夏玉米产量
N300: 施氮量为 300 kg hm–2; N225: 施氮量为 225 kg hm–2; N150: 施氮量为 150 kg hm–2; N75: 施氮量为 75 kg hm–2; N0: 施氮量为 0 kg hm–2; W2: 拔节期和抽雄期共灌水 800 m3hm–2; W1: 拔节期灌水 400 m3hm–2; W0: 不灌水;-: 产量减少; W: 灌水量; N: 施氮量; W×N: 灌水量×施氮量。同列数据后不同小写字母表示同一年份不同处理间差异显著(< 0.05)。NS表示无显著差异, *表示在0.05概率水平差异显著, **表示在0.01概率水平差异显著, ***表示在0.001概率水平差异显著。
N300: nitrogen application rate was 300 kg hm–2; N225: nitrogen application rate was 225 kg hm–2; N150: nitrogen application rate was 150 kg hm–2; N75: nitrogen application rate was 75 kg hm–2; N0: no nitrogen application; W2: irrigated 800 m3hm–2in jointing and tasseling stage; W1: irrigated 400 m3hm–2in jointing stage; W0: no irrigation;-: yield reduction. W: irrigation amount; N: nitrogen application; W×N: irrigation × nitrogen.Values followed by different lowercase letters within a column indicate significant difference among treatments in the same year at< 0.05. NS: not significant. *, **, and *** indicate significant difference at the 0.05, 0.01, and 0.001 probability levels, respectively.
表3 不同水氮减量处理下夏玉米地上部吸氮量和氮素利用效率
N300: 施氮量为300 kg hm–2; N225: 施氮量为225 kg hm–2; N150: 施氮量为150 kg hm–2; N75: 施氮量为75 kg hm–2; N0: 施氮量为 0 kg hm–2; W2: 拔节期和抽雄期共灌水 800 m3hm–2; W1: 拔节期灌水 400 m3hm–2; W0: 不灌水; W: 灌水量; N: 施氮量; W×N: 灌水量×施氮量; N uptake: 夏玉米地上部吸氮量; NAE: 氮肥农学效率; NRE: 氮肥回收效率; NPFP: 氮肥偏生产力。同列数据后不同小写字母表示同一年份不同处理间差异显著(< 0.05)。NS表示无显著差异, *表示在0.05概率水平差异显著, **表示在0.01概率水平差异显著, ***表示在0.001概率水平差异显著。
N300: nitrogen application rate was 300 kg hm–2; N225: nitrogen application rate was 225 kg hm–2; N150: nitrogen application rate was 150 kg hm–2; N75: nitrogen application rate was 75 kg hm–2; 0: no nitrogen application; W2: irrigated 800 m3hm–2at jointing and tasseling stages; W1: irrigated 400 m3hm–2at jointing stage; W0: no irrigation; W: irrigation amount; N: nitrogen application; W×N: irrigation × nitrogen, N uptake: aboveground nitrogen uptake of summer maize; NAE: nitrogen agronomic efficiency; NRE: nitrogen recovery efficiency; NFPF: N partial factor productivity. Values followed by different lowercase letters within a column indicate significant difference among treatments in the same year at< 0.05. NS: not significant. *, **, and *** indicate significant difference at the 0.05, 0.01, and 0.001 probability levels, respectively.
2.3 不同水氮减量处理对土壤硝态氮含量及其残留的影响
由图2可知, 在同一灌水水平下, 随施氮量减少, 夏玉米田0~200 cm、0~300 cm土层土壤硝态氮含量呈递减趋势, 与N300相比, N75、N0硝态氮含量始终处于相对较低的水平, N300累积峰含量大约为50.2 mg kg–1。从灌水量看, W2、W1土壤硝态氮含量随土层的加深逐渐增大, W0则逐渐减小。2018—2019年0~60 cm土层中硝态氮含量W0和W1大于W2, 2020年0~100 cm土层中硝态氮含量W0和W1大于W2, 60 cm、100 cm以下则是W2大于W1、W0。2018年W2、W1、W0累积峰分别出现在120~160 cm、80~120 cm、40~60 cm; 2019年W2处理累积峰不明显, 可能出现在200 cm以下, W1下N300在80、180 cm土层出现累积峰, N225、N150在120~140 cm土层出现累积峰, W0下在60~80 cm出现累积峰; 2020年W2下硝态氮含量在0~100 cm、200~300 cm土层时较高, W1、W0则分别在200~220 cm、80~100 cm出现累积峰, 由此说明, 随着试验年份的推进, 硝态氮累积向下层土壤移动, 灌水量增大显著加快硝态氮向下淋洗。不同水氮减量处理下, N300和N225硝态氮含量显著高于N150、N75、N0, 且随着灌水量的变化波动幅度明显, 说明施氮和灌水显著影响硝态氮含量和向下的迁移, 施氮量增大, 含量越大, 灌水增多, 向下迁移加快。
图2 2018–2020年玉米收获后0~200 cm、0~300 cm土层土壤硝态氮含量剖面图
N300: 施氮量为300 kg hm–2; N225: 施氮量为225 kg hm–2; N150: 施氮量为150 kg hm–2; N75: 施氮量为75 kg hm–2; N0: 施氮量为0 kg hm–2; W2: 拔节期和抽雄期共灌水800 m3hm–2; W1: 拔节期灌水 400 m3hm–2; W0: 不灌水, *表示在0.05概率水平差异显著, **表示在0.01概率水平差异显著, ***表示在0.001概率水平差异显著。
N300: nitrogen application rate was 300 kg hm–2; N225: nitrogen application rate was 225 kg hm–2; N150: nitrogen application rate was 150 kg hm–2; N75: nitrogen application rate was 75 kg hm–2; 0: no nitrogen application; W2: irrigated 800 m3hm–2in jointing and tasseling stage; W1: irrigated 400 m3hm–2at jointing stage; W0: no irrigation. *, **, and *** indicate significant difference at the 0.05, 0.01, and 0.001 probability levels, respectively.
由图3和图4可知, 施氮量和灌水量对硝态氮残留有显著影响。随着施氮量的减小, 2018—2020 年硝态氮残留量不断减少, 因此, 减少施氮量可以减小硝态氮淋溶风险。从灌水来看, 2018—2020年0~100 cm硝态氮残留量为W0>W1>W2, 而2018— 2019年在100~200 cm土层则是W2>W1>W0, 2020年100~200 cm土层为W0>W1>W2, 200~300 cm为W2>W1>W0, 其原因可能是因为灌水使硝态氮不断向下层土壤迁移。不同水氮减量处理下, 相比对照W2N300来说, 其他水氮减量处理硝态氮残留减小15.6%~91.9%。
图3 不同水氮减量处理下玉米田0~200 cm土层硝态残留量
N300: 施氮量为300 kg hm–2; N225: 施氮量为225 kg hm–2; N150: 施氮量为 150 kg hm–2; N75: 施氮量为75 kg hm–2; N0: 施氮量为0 kg hm–2; W2: 拔节期和抽雄期共灌水800 m3hm–2; W1: 拔节期灌水400 m3hm–2; W0: 不灌水。同列数据后不同小写字母表示同一年份不同处理间差异显著(< 0.05)。
N300: nitrogen application rate was 300 kg hm–2; N225: nitrogen application rate was 225 kg hm–2; N150: nitrogen application rate was 150 kg hm–2; N75: nitrogen application rate was 75 kg hm–2; 0: no nitrogen application; W2: irrigated 800 m3hm–2at jointing and tasseling stages; W1: irrigated 400 m3hm–2at jointing stage; W0: no irrigation. Values followed by different lowercase letters within a column indicate significant difference among treatments in the same year at< 0.05.
图4 不同水氮减量处理下玉米田0~300 cm土层硝态残留量
N300: 施氮量为 300 kg hm–2; N225: 施氮量为 225 kg hm–2; N150: 施氮量为 150 kg hm–2; N75: 施氮量为 75 kg hm–2; N0: 施氮量为 0 kg hm–2; W2: 拔节期和抽雄期共灌水 800 m3hm–2; W1: 拔节期灌水 400 m3hm–2; W0: 不灌水。同列数据后不同小写字母表示同一年份不同处理间差异显著(< 0.05)。
N300: nitrogen application rate was 300 kg hm–2; N225: nitrogen application rate was 225 kg hm–2; N150: nitrogen application rate was 150 kg hm–2; N75: nitrogen application rate was 75 kg hm–2; 0: no nitrogen application; W2: irrigated 800 m3hm–2at jointing and tasseling stages; W1: irrigated 400 m3hm–2at jointing stage; W0: no irrigation. Values followed by different lowercase letters within a column indicate significant difference among treatments in the same year at< 0.05.
2.4 不同水氮减量处理产量、氮肥利用、土壤硝态氮残留量与施氮量的关系
3年试验结果(表4)表明, 施氮量与产量和土壤硝态氮残留呈极显著二次函数的关系(<0.001), 施氮量与氮肥农学效率、氮肥回收效率、氮肥偏生产力显著线性相关(<0.01), 均随施氮量的减少呈增加趋势。
2.5 不同水氮组合的综合效应评价
2.5.1 权重确定 通过层次分析法确定权重, 产量、吸氮量、NAE、NRE、NPFP和硝态氮所占权重依次为0.619、0.026、0.031、0.045、0.054和0.226, 由于层次分析法是评价者对评价问题本质的分析和判断, 因此, 此种方法指标3年权重相同。
熵权法是一种客观分析方法, 其中产量、吸氮量、NAE、NRE、NPFP为正向指标, 硝态氮为负向指标, 通过SPSSPRO软件分析所得指标权重分别为: 2018年产量、吸氮量、NAE、NRE、NPFP、硝态氮分别为0.108、0.114、0.198、0.213、0.247、0.121; 2019年分别为0.457、0.064、0.109、0.117、0.144、0.108; 2020年分别为0.094、0.139、0.186、0.184、0.225、0.173。
表4 夏玉米产量、氮肥利用率和0~200 cm土层硝态氮残留量与施氮量的回归分析模型
W2: 拔节期和抽雄期共灌水800 m3hm–2; W1: 拔节期灌水 400 m3hm–2; W0: 不灌水, Y: 产量; NR: 硝态氮残留量; NAE: 氮肥农学效率; NRE: 氮肥回收效率; NPFP: 氮肥偏生产力。*表示在0.05概率水平差异显著,**表示在0.01概率水平差异显著,***表示在0.001概率水平差异显著。
W2: irrigated 800 m3hm–2at jointing and tasseling stages; W1: irrigated 400 m3hm–2at jointing stage; W0: no irrigation; Y: yield; NR: residual nitrate; NAE: nitrogen agronomic efficiency; NRE: nitrogen recovery efficiency; NFPF: N partial factor productivity.*,**, and***indicate significant difference at the 0.05, 0.01, and 0.001 probability levels, respectively.
由于层次分析法和熵权法所得权重之间存在差异, 因此采用博弈论组合赋权的方法确定指标的最终权重, 2018年产量、吸氮量、NAE、NRE、NPFP、硝态氮权重分别为0.337、0.120、0.108、0.122、0.144、0.168; 2019年分别为0.568、0.047、0.057、0.065、0.074、0.188; 2020年分别为0.336、0.088、0.107、0.125、0.147、0.197。
2.5.2 不同水氮组合的综合评价方法-TOPSIS法
图5表明贴合度Di与产量B11、吸氮量B21、氮肥农学效率B22、氮肥回收效率B23的相关性达到极显著水平(<0.001), 贴合度Di与氮肥偏生产力B24的相关性达到显著水平(<0.01), 由此说明利用TOPSIS法确定水氮减量对夏玉米综合评价指标的影响是可行的。
通过TOPSIS法从高产、高效、环保3方面出发, 确定不同水氮组合的综合评价指标排序(表5), 3年综合排序排名前5的处理分别为W1N225、W2N150、W1N150、W2N225、W2N75, W1N225、W2N150、W1N150、W2N75、W1N300, W1N150、W1N225、W1N300、W2N75、W2N150。在同一灌水水平下, 与N300相比, N225、N150时综合指标分别增大10.3%、14.4%, 在同一施氮水平下, 与W2相比, 综合指标W1增大4.1%, 由此可知, 水氮减量优于传统水氮管理模式。氮肥减量25%~50%、灌水减少50%时各指标评价值最高, 因此, 在关中平原灌溉区可以通过水氮减量实现玉米高产、节约资源和保护环境的目标。由W2N300、W2N150、W1N225、W1N150、W0N300、W0N0可知, 水氮协同调控夏玉米综合指标, 水氮减量(中水中肥)优于高水高肥, 高水高肥优于低水低肥,高水低肥优于低水高肥, 因此, 在关中平原可以适当减少施氮量来提高夏玉米综合评价值。
将综合评价值与施氮量进行拟合(图6), 综合评价值与施氮量呈显著的二次函数的关系, 在同一灌水水平下, 综合评价值随施氮量的增加先增后降。图6中水氮减量(中水中肥)综合评价值维持在较高水平, 且高水低肥优于低水高肥, 因此为了提高综合评价值可以施氮减少施氮量。在2018、2019、2020年W2、W1、W0水平下施氮量分别为182.6、180.6、183.9, 184.7、210.5、195.9, 159.8、208.8和153.7 kg hm–2时, 综合评价值最高为0.74、0.82、0.45, 0.76、0.80、0.67, 0.77、0.81、0.60, W2、W1、W0下减氮41.3%、33.3%、40.7%仍可达到最优。通过模型模拟寻优, 得出在灌水量为W1 (400 m3hm–2), 施氮量为200 kg hm–2时综合评价值最优。
图5 贴合度与不同指标的相关性分析
Di: 贴合度; B11: 产量; B21: 吸氮量; B22: 氮肥农学效率; B23: 氮肥回收效率; B24: 氮肥偏生产力; B31: 硝态残留量。*表示在0.05概率水平差异显著, **表示在0.01概率水平差异显著, ***表示在0.001概率水平差异显著。
Di: the degree of fit; B11: maize; B21: aboveground nitrogen uptake of summer maize; B22: nitrogen agronomic efficiency; B23: nitrogen recovery efficiency; B24: N partial factor productivity; B31: residual nitrogen. *, **, and *** indicates significant difference at the 0.05, 0.01, and 0.001 probability levels, respectively.
表5 基于TOPSIS 法确定的不同水氮处理的贴近度和综合排序
TOPSIS: 优劣解距离法; N300: 施氮量为 300 kg hm–2; N225: 施氮量为 225 kg hm–2; N150: 施氮量为 150 kg hm–2; N75: 施氮量为 75 kg hm–2; N0: 施氮量为0 kg hm–2; W2: 拔节期和抽雄期共灌水800 m3hm–2; W1: 拔节期灌水400 m3hm–2; W0: 不灌水; D+: 理想解和处理间距离; D–: 逆理想解和处理间距离; Di: 贴合度。同列数据后不同小写字母表示同一年份不同处理间差异显著(< 0.05)。
TOPSIS:technique for order preference by similarity to an ideal solution; N300: nitrogen application rate was 300 kg hm–2; N225: nitrogen application rate was 225 kg hm–2; N150: nitrogen application rate was 150 kg hm–2; N75: nitrogen application rate was 75 kg hm–2; 0: no nitrogen application; W2: irrigated 800 m3hm–2at jointing and tasseling stages; W1: irrigated 400 m3hm–2at jointing stage; W0: no irrigation; D+: the distance between the ideal solution and the treatment; D–: the distance between the inverse ideal solution and the treatment; Di: the degree of fit. Values followed by different lowercase letters within a column indicate significant difference among treatments in the same year at< 0.05.
图6 不同灌水条件下综合评价贴合度与施氮量的关系
W2: 拔节期和抽雄期共灌水 800 m3hm–2; W1: 拔节期灌水 400 m3hm–2; W0: 不灌水。
W2: irrigated 800 m3hm–2at jointing and tasseling stages; W1: irrigated 400 m3hm–2at jointing stage; W0: no irrigation.
3 讨论
3.1 水氮减量对夏玉米产量和氮素利用的影响
施氮和灌水是作物增产的主要手段, 二者相互协调可以促使玉米高产。农业生产中多通过增施氮肥来增产, 但实际生产中, 作物产量却随施氮量的增加而减少[28], 玉米生育期内耗水量高, 只有合理灌溉才可以提高氮素利用率并增产[29]。本研究发现, 减水50%和减氮25%~50%产量小幅增加, 这与邢维芹等[30]在半干旱地区进行的研究结果相似, 适宜水氮减量玉米产量下降幅度小于15.26%, 且水分利用率提高。本研究中减水50%和减氮25%~50% NAE、NRE、NPFP随施氮量减少呈增加趋势, 氮素利用保持在较高水平, 植株吸氮量与传统水氮管理模式无显著差异。前人研究发现, 与习惯灌水和施氮量相比, 水氮减量处理提高氮素利用率且对夏玉米产量无显著影响[31]; 减少施氮量且水分为田间持水量的75% ± 5%的土壤条件时, 可以提高植株氮素积累量, 合理的水氮运筹方式有利于高产群体的构建[32]; 适宜减少施氮量显著提高籽粒氮素积累, 提高植株氮素吸收[14]。造成以上现象的原因: 一是适宜氮肥用量可以提高叶片的叶绿素含量, 提高叶片光合碳同化能力, 延长光合时间, 促进光合同化物向籽粒转移, 进而提高产量[33]。二是过量施氮其增产效果会随施氮量增加逐渐减弱, 在土壤水分不足时, 水分制约氮肥发挥效用, 当水分过多则会增加氮素损失, 进而氮素利用降低[34]。因此, 在关中平原玉米生产中可以适当减少灌溉量和施氮量, 会有小幅增产, 且提高氮素利用效率, 减少资源浪费。
3.2 水氮减量对土壤硝态氮残留的影响
施氮和灌水是影响土壤硝态氮含量及其残留的重要因素, 二者增加会使土壤中硝态氮的残留量增加[35-37], 同时硝态氮向下迁移的速度加快[38]。本研究中, 土壤硝态氮含量N300>N225>N150>N75>N0, 且残留量W2>W1>W0。2019、2020年灌水处理N300下硝态氮随土层的加深出现2个累积峰。其原因可能是土壤质地导致, 塿土中有黏化层的存在(多在60~140 cm), 黏化层上硝态氮分部较均匀, 高施氮量下硝态氮迁移到黏化层以下迁移变快, 在较深土层则会出现第2个累积峰[35]。同时, 0~100 cm、100~200 cm、200~300 cm土层硝态氮残留量出现显著的差异。其原因可能是由于土壤中NO3–-N的运动一般与水分同步或略滞后[39], 灌水量的差异致使不同土层残留量的不同。因此, 在关中平原农业生产中实施水氮减量可以减少土壤中硝态氮含量和残留,降低土壤、地下水污染的风险。
3.3 基于TOPSIS的水氮减量方案模拟寻优
水氮管理模式的优选, 通常是水氮用量对作物产量、氮素利用等的单变量回归分析, 从养分资源效率最大化目标确立适宜的水氮用量, 单变量回归多适用于指标较少的评价, 不适用于多指标判定。前人通过综合评价体系建立多变量回归分析探究作物对水肥用量的响应[40-41], 研究表明, 各指标对应的最佳水氮组合并不是水氮投入越多越优。本研究通过综合评价体系建立多变量回归分析, 运用数学方法定量化研究产量、氮素利用、环境等多指标对水肥用量的综合效果, 确定最优水氮减量方案的灌溉量和施氮量 (灌水为400 m3hm–2, 施氮200 kg hm–2)。前人通过综合评价法[42]和综合指数法[9]的研究也表明, 综合得分随施氮量的增加呈先增后降的趋势, 选取综合得分高于0.8的确定为适宜施氮量, 推荐施氮186~225 kg hm–2可以实现高产高效、农田环境友好氮肥体系; 综合指标值定量地显示了减氮20%的包膜尿素应用综合效果最佳。因此, 在关中平原夏玉米拔节期灌水量400 m3hm–2和总施氮量200 kg hm–2可以实现高产高效、资源节约、保护环境的目标。
4 结论
在关中平原灌区, 在目前常规管理中灌水800 m3hm–2和施氮量300 kg hm–2基础上, 灌水减量50%、施氮减量25%~50%时, 夏玉米获得较高产量, 氮素利用效率保持较高水平, 且减少土壤硝态氮残留。水氮减量显著影响夏玉米综合评价指标, 综合各指标对水氮减量的响应, 通过TOPSIS法对水氮减量方案模拟寻优, 拔节期灌水量400 m3hm–2和总施氮量200 kg hm–2可以作为关中平原夏玉米节水节肥、高效环保的生产模式。
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Water and nitrogen reduction scheme optimization based on yield and nitrogen utilization of summer maize
LI Hui, WANG Xu-Min, LIU Miao, LIU Peng-Zhao, LI Qiao-Li, WANG Xiao-Li, WANG Rui, and LI Jun*
1College of Agronomy, Northwest A&F University, Yangling 712100, Shaanxi, China;2Key Laboratory of Crop Physio-ecology and Tillage Science in Northwestern Loess Plateau, Ministry of Agriculture and Rural Affairs, Yangling 712100, Shaanxi, China
The objective of this study is to solve the problems of excessive water and nitrogen input in current summer maize cropping system and lacking comprehensiveevaluation approach and evaluatethe current water and nitrogen management scheme for yield, nitrogen utilization of summer maize and soil nitrate nitrogen content. AHP, entropy method, and game theory were combined to determine index weight, TOPSIS was used to evaluate water and nitrogen reduction scheme, thus the results can provide a theoretical basis for water-saving, nitrogen-reducing and high efficient cultivation scheme of summer maize in Guanzhong plain. The two-factor split-plot field experiment during 2018–2020 was conducted in Yangling, Shaanxi province, where three irrigation levels were traditional 800 m3hm–2(W2) as the control, reduced to 400 m3hm–2(W1), and no irrigation (W0). Each water treatment was the five N rate treatments [300 kg hm–2(N300) as the control, reduced 25% (225 kg hm–2), reduced 50% (150 kg hm–2), reduced 75% (75 kg hm–2), and no N fertilizer (0)]. Maize yield, nitrogen use efficiency, and soil nitrate nitrogen content under different water and nitrogen reduction treatments were analyzed and to choose optimal scheme with modeling by TOPSIS. Compared with W2N300 (CK), W1N225 had best effect on yield, and increased significantly by 5.4%. Meanwhile, W2N225, W2N150, and W1N150 had significantly effect on yield, and increased significantly by 2.4%, 0.7%, and 0.3%, respectively. W1N225 and W1N150 enhanced the N-use efficiency, agronomic efficiency, and partial factor productivity, andincreased significantly by 29.7%, 16.2%, 24.5%; 36.5%, 25.4%, 28.8%; 53.4%, 36.7%, 32.8%; 46.5%, 35.2%, 47.4%; 43.6%, 37.3%, 48.0%; and 66.9%, 43.1%, 54.5% than CK in 2018, 2019, 2020, respectively. W1N225, W1N150 reduced soil nitrate nitrogen leaching, and decreased by 28.6% and 53.8% than CK, respectively.Using TOPSIS for comprehensive evaluation, it was found that the evaluation value of each index was the highest when nitrogen fertilizer was reduced by 25%–50% (with nitrogen application rate of 150–225 kg hm–2) and irrigation water was reduced by 50% (irrigated 400 m3hm–2in jointing stage). Water and nitrogen reduction (medium fertilizer in middle water) were better than high water and high fertilizer, high water and high fertilizer were better than low water and low fertilizer, and high water and low fertilizer were better than low water and high fertilizer. Through TOPSIS optimization, the comprehensive evaluation value was the best when the irrigation amount was W1 (irrigated 400 m3hm–2at jointing stage) and the nitrogen application amount was 200 kg hm–2. Therefore, reduced irrigation (irrigated 400 m3hm–2in jointing stage) and reduced 33.3% nitrogen (with nitrogen application rate of 200 kg hm–2) mode can be used to realize the water-saving and nitrogen reduction production of summer maize in Guanzhong Plain.
summer maize; irrigation and nitrogen fertilizer reduction; nitrogen use efficiency; yield; technique for order preference by similarity to an ideal solution
10.3724/SP.J.1006.2023.23046
本研究由国家科技支撑计划项目(2015BAD22B02)和国家自然科学基金项目(31801300)资助。
The study was supported by the National Science and Technology Support Program of China (2015BAD22B02) and the National Natural Science Foundation of China (31801300).
李军, E-mail: junli@nwsuaf.edu.cn
E-mail: 17852021052@163.com
2022-06-01;
2022-09-05;
2022-09-17.
URL: https://kns.cnki.net/kcms/detail/11.1809.S.20220916.1121.004.html
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).