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作物学报, 2024, 50(5): 1300-1311 DOI: 10.3724/SP.J.1006.2024.31035

耕作栽培·生理生化

稻麦复种模式下氮肥与稻秸互作对小麦产量和N2O排放影响及推荐施肥研究

陆汝华,1, 王文轩2, 曹强1, 田永超1, 朱艳1, 曹卫星1, 刘小军,1,*

1南京农业大学国家信息农业工程技术中心 / 智慧农业教育部工程研究中心 / 农业农村部农作物系统分析与决策重点实验室 / 江苏省信息农业重点实验室, 江苏南京 210095

2人文与社会发展学院, 江苏南京 210095

Research on the effects of nitrogen fertilizer and rice straw return on wheat yield and N2O emission and recommended fertilization under rice-wheat rotation pattern

LU Ru-Hua,1, WANG Wen-Xuan2, CAO Qiang1, TIAN Yong-Chao1, ZHU Yan1, CAO Wei-Xing1, LIU Xiao-Jun,1,*

1National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University / Engineering and Research Center for Smart Agriculture, Ministry of Education / Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs / Jiangsu Key Laboratory for Information Agriculture, Nanjing 210095, Jiangsu, China

2College of Humanities & Social Development, Nanjing 210095, Jiangsu, China

通讯作者: 刘小军, E-mail:liuxj@njau.edu.cn, Tel: 025-84396804

收稿日期: 2023-06-4   接受日期: 2023-10-23   网络出版日期: 2023-11-30

基金资助: 国家重点研发计划项目(2022YFD2301402)
南京农业大学三亚研究院(NAUSY-ZD01)
国家自然科学基金项目(32071903)

Corresponding authors: E-mail:liuxj@njau.edu.cn, Tel: 025-84396804

Received: 2023-06-4   Accepted: 2023-10-23   Published online: 2023-11-30

Fund supported: National Key Research and Development Program of China(2022YFD2301402)
Hainan Institute of Nanjing Agricultural University(NAUSY-ZD01)
National Natural Science Foundation of China(32071903)

作者简介 About authors

E-mail:2020101177@njau.edu.cn

摘要

优化氮肥施用和秸秆还田技术为途径的农业管理措施被认为是提升农业可持续性的有效手段, 然而当前关于氮肥和秸秆还田对小麦产量和N2O排放影响的研究仍十分有限。为此, 本研究基于2000—2022年发表的关于长江中下游流域氮肥和秸秆投入下小麦产量和N2O排放变化的文献, 运用随机森林建模, 定量分析氮肥和秸秆还田对小麦产量和N2O排放的影响, 并结合情景设置进行了特定地点的小麦产量和N2O排放模拟, 同时评估了碳排放强度(CEE)和净生态系统经济效益(NEEB)。结果表明, 建立的区域尺度小麦产量与N2O排放对氮秸互作响应的随机森林模型, 验证结果R2分别为0.66和0.65, RMSE分别为0.70和1.11。结果表明施氮量和土壤有机质是影响小麦产量和N2O排放的重要因素。综合来看, 达到最大产量所需的氮肥量为208~212 kg hm-2, 达到最小CEE所需的氮肥量为113~130 kg hm-2, 达到最高的NEEB所需的氮肥量为202~205 kg hm-2, 其中在6.75 t hm-2的秸秆投入下施用202 kg hm-2的氮肥可以获得最高的生态收益1.37万元。优化氮肥和秸秆投入具备减少作物碳排放强度并获得最大净生态环境效益的潜力。

关键词： 施氮量; 秸秆投入; 小麦; N2O; 排放模型; 推荐施肥

Abstract

The optimization of agricultural practices such as nitrogen and straw input may be an effective option for maintaining environmental sustainability. However, previous studies on the effects of nitrogen and straw inputs on wheat growth and N2O emission reduction were limited. Therefore, the present study was based on the literature published from 2000 to 2022 about wheat yield and N2O emissions under different nitrogen and straw inputs amendment in the middle and lower reaches of the Yangtze River, a random forest (RF) model of wheat yield and N2O emission was constructed. And the influence of nitrogen and straw inputs on wheat yield and N2O emissions was quantified. Based on the developed model, wheat yield and N2O emission simulations at the experimental site were carried out in combination with scenario settings, and the carbon emission intensity (CEE) and net ecosystem economic benefits (NEEB) were evaluated. The results were as follow: On the regional scale, an RF model was established for the response of wheat yield and N2O emission to the application of nitrogen fertilizer and straw returning. The verification results were R2 of 0.66 and 0.65, and RMSE of 0.70 and 1.11, respectively. Quantifying the importance of independent variables showed that nitrogen application rate and soil organic matter were essential for yield and N2O models. For nitrogen fertilizer and straw management under different targets, the amount of nitrogen fertilizer required to achieve the highest yield was 208-212 kg hm-2, the amount of nitrogen fertilizer required to achieve the minimum CEE was 113-130 kg hm-2, and the amount of nitrogen fertilizer required to achieve the highest NEEB was 202-205 kg hm-2, of which the highest ecological benefit of 13,669.18 CHY could be obtained by applying 202 kg hm-2 nitrogen fertilizer under the straw input of 6.75 t hm-2. Our results indicate that optimizing nitrogen fertilizer and straw inputs has the potential to reduce crop carbon emission intensity and maximize net ecological and environmental benefits.

Keywords： nitrogen application rate; straw inputs; wheat; N2O; emission model; fertilizer recommendation

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本文引用格式

陆汝华, 王文轩, 曹强, 田永超, 朱艳, 曹卫星, 刘小军. 稻麦复种模式下氮肥与稻秸互作对小麦产量和N2O排放影响及推荐施肥研究. 作物学报, 2024, 50(5): 1300-1311 DOI:10.3724/SP.J.1006.2024.31035

LU Ru-Hua, WANG Wen-Xuan, CAO Qiang, TIAN Yong-Chao, ZHU Yan, CAO Wei-Xing, LIU Xiao-Jun. Research on the effects of nitrogen fertilizer and rice straw return on wheat yield and N2O emission and recommended fertilization under rice-wheat rotation pattern. Acta Agronomica Sinica, 2024, 50(5): 1300-1311 DOI:10.3724/SP.J.1006.2024.31035

小麦对粮食安全做出了巨大贡献[1], 提供了人类饮食中约20%的热量和蛋白质[2]。农业作为一氧化二氮(N2O)的最大排放源, 占全球人为N2O排放量的56%~81%, 对全球气候变化具有重大影响[3]。农田管理实践(如秸秆投入和施肥)会影响土壤中的生化和水文条件, 进而调节作物生长和N2O排放[4]。长江中下游地区是我国重要的小麦主产区, 研究不同的氮肥和秸秆投入如何影响小麦产量和N2O排放对于制定区域农业可持续施肥管理至关重要。

作物产量的增加依赖于基因型、环境因素(包括气候和土壤条件)和农业管理之间复杂的相互作用。近年来, 作物生产依赖于越来越多的氮肥投入来追求更高的产量, 但作物生长速度和产量的增加并没有与氮肥施用量的增加相匹配[5]。与此同时, 施用尿素等含氮化肥在维持作物产量的同时, 也为N2O的合成提供了基质。已有研究表明, 氮素含量与直接N2O排放之间呈线性函数关系或者非线性关系[6⇓-8], 这都表明不断增加氮肥施用量会导致N2O排放量迅速增加[9]。此外, 秸秆还田对土壤N2O排放的影响也十分复杂, 秸秆还田显着影响生物地球化学C和N循环, 从而影响N-痕量气体的产生和排放, 作物秸秆滞留可能通过影响相对C和N的可用性以及在土壤通气状态调节土壤N2O通量方面发挥多种作用[10-11]。一般来说, 秸秆还田会刺激N2O的产生, 因为作物秸秆分解为硝化菌/反硝化菌提供了底物, 促进了反硝化进程[12]。此外, 秸秆还田对N2O排放有负面影响或没有显着影响[13]。最近的研究表明, 氮肥与秸秆配合使用是提高氮素利用效率和土壤肥力的有效策略[14], 因为组合施用可以克服施用单一肥料的缺点, 可以提高作物产量, 提高土壤肥力并缓解温室气体排放。Huang等[15]的研究发现, 麦玉轮作中优化的氮肥施用量与秸秆还田相结合, 大大增加了产量和降低了产量尺度的N2O排放。Akhtar等[16]的研究也发现9000 kg hm-2的玉米秸秆还田配合减氮20%被认为是提高作物产量和减少土壤N2O排放的可行性技术。在长江中下游地区, 氮肥和秸秆合理配施, 是否可以平衡土壤碳氮, 改善农田生态效应, 并显著提高产量, 是本文的主要研究目标。

小麦产量形成和N2O排放通常受到区域尺度下不同土壤、气象和管理措施的综合影响, 而秸秆和氮肥投入广泛应用的前提, 是揭示其在不同地区的小麦增产与N2O减排效应的驱动因素, 才能更好地权衡小麦的养分需求和环境影响并进行指导施肥管理, 从而实现N2O排放减少和作物产量稳定。目前探索产量措施响应的田间试验通常只设置固定的氮肥和秸秆投入梯度和选用特定地点的小麦品种, 因此结果很难外推到所有的生长环境中。基于大型数据集的统计方法可以更全面地了解产量-投入关系, 机器学习(Machine learning, ML)模型提供了一种强大的方法来研究作物管理和生长环境之间的复杂相互作用, 因为它们能够基于集成学习方法处理预测变量与目标变量之间的复杂关系, 被广泛应用在N2O排放量预测[17]、产量预测[18]和施肥推荐[19]。然而, 目前鲜少关于应用ML模型预测氮秸互作下的麦田N2O排放和产量并用于推荐施肥措施的报道。鉴于此, 本研究首先基于随机森林(Random forest, RF)算法对长江中下游区域不同氮肥和秸秆投入对小麦产量和N2O排放变化响应进行建模、验证和评估。然后利用验证的模型和不同氮肥和秸秆投入情景设置对小麦产量和N2O累积排放量进行估计, 并评估不同氮肥和秸秆投入下的碳排放强度(Carbon emission efficiency, CEE)和净生态系统经济效益(Net ecosystem economic benefits, NEEB), 为根据不同目标的环境友好型的氮肥和秸秆投入措施提供技术支持。

1 材料与方法

1.1 数据来源

本研究检索了2000—2022年在Web of Science和中国知网关于稻麦轮作区下麦田N2O排放、氮肥处理、秸秆投入、产量的中英文论文。以下搜索词作为关键词: “秸秆还田” “氮肥” “N2O” “温室气体” “产量”, “Straw return” “nitrogen fertilizer” “greenhouse gas” “GHG” “GWP”。不同的关键词之间使用“AND”表明并列关系。符合标准的研究被纳入并进行分析: (1) 所有摘录文献必须是在野外开展的田间试验, 且用的是静态箱-气相色谱法; (2) 还田的秸秆必须是水稻秸秆且麦田研究必须在长江中下游地区; (3) 文献的结果都可以直接从表或图或文本中获取。

搜集相关文献后, 需要对录入的数据进行异常值剔除的预处理, 数据库从超过323篇下载的文献中, 筛选了30篇符合标准的文献[20⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓-49], 涉及产量观测数据为80条, 氧化亚氮观测数据为184条。文献数据地点主要分布在江苏省、安徽省和湖北省。其中, 江苏省的数据地点主要包括: 南京市(32.0°N, 118.8°E)、(32.48°N, 118.6°E)、(31.93°N, 118.98°E), 镇江市(31.97°N，119.3°E), 扬州市(32.58°N, 119.2°E)、(32.58°N, 119.7°E), (苏州市(31.4°N, 120.42°E)、(31.53°N, 120.7°E)、 (31.55°N, 120.62°E)、(31.53°N, 120.92°E)、(31.53°N, 120.68°E), 南通市(31.68°N, 121.9°E), 无锡市(31.45°N, 120.42°E)。安徽省的数据点主要包括: 滁州市(32.0°N, 118.13°E), 芜湖市(31.15°N, 118.13°E), 巢湖市(31.65°N, 117.68°E)、(31.6S°N, 117.67°E)。湖北省的数据点主要包括: 黄冈市(30.02°N, 115.57°E), 荆门市(30.88°N, 112.8°E), 襄阳市(32.02°N, 112.07°E)。

1.2 数据分析

1.2.1 CEE和NEEB的计算

CEE是单位产量下的碳排放强度变化, 又称碳排放强度, 由于碳排放强度变化的计量单元主要是旱地作物化肥施用引起的N2O直接排放, CEE计算如下:

CEE = N2O/Yield

N2O表示麦田N2O累积排放量并转化为二氧化碳当量(kg CO2-eq hm-2), Yield表示小麦产量(kg hm-2)。

净生态系统经济预算(NEEB)是根据粮食产量成本、农业投入成本和碳成本计算出来的[50], 如下所示:

$NEEB = 粮食产量成本–农业活动成本–碳成本$

在这项研究中, 粮食产量成本是根据当前粮食价格(小麦, 2286 CHY t-1)和粮食产量计算得出的[51]。农业活动成本包括氮肥成本(3.6 CHY kg-1 N)[52], 碳成本是当前碳贸易价格(56.38 CHY t-1 CO2-eq)和GWP的乘积[53]。在本研究中, 不考虑机械耕作、小麦种子、其他肥料、灌溉、化学农药(除草剂+杀虫剂+杀菌剂)、秸秆处理和机械收割等的农业活动成本。

1.2.2 皮尔逊相关分析

利用IBM SPSS Statistic 26进行皮尔逊相关分析, 本研究的重点是使用皮尔逊相关系数(公式3)分析2个变量之间的关系, 其值介于-1和+1之间。

$r=1-\frac{\sum_{i=1}^{n}\left(x_{i}-x\right)\left(y_{i}-y\right)}{\sqrt{\sum_{i=1}^{n}\left(x_{i}-x\right)^{2}} \sqrt{\sum_{i=1}^{n}\left(y_{i}-y\right)^{2}}}$

式中, n是总样本大小, xi和yi是用索引I的单个样本点, x、y分别表示是样本的平均值。

1.2.3 RF模型的构建

RF是经典的监督学习算法, 本研究中的RF算法使用Python软件(版本3.8)实现, 并通过计算每个特征的平均不纯度减少量对特征重要性进行排序[54]。

1.2.4 模型的检验与评价

本研究采用标准差(Standard deviation, SD)和变异系数(Coefficient of variation, CV)被用来表征总试验数据的分离分散程度。CV越大, 则所有数据包含的可能性就越多。SD和CV的计算如下:

$\mathrm{SD}=\sqrt{\frac{1}{K-1} \sum_{i=1}^{K}\left(x_{i}-\bar{x}\right)^{2}}$
$\mathrm{CV}=\frac{\mathrm{SD}}{\text { Mean }}$

本研究通过比较实际值和模拟值之间1∶1线的决定系数(R2)、均方根误差(RMSE), 并结合1∶1图来描述模型对小麦生产力和温室气体排放的预测效果。具体计算如下:

$R^{2}=r^{2}=\left(\frac{\sum_{i}^{n}\left(O_{i}-\bar{O}\right)\left(P_{i}-\bar{P}\right)}{\sqrt{\sum_{i}^{n}\left(O_{i}-\bar{O}\right)^{2} \sum_{i}^{n}\left(P_{i}-\bar{P}\right)^{2}}}\right)^{2}$
$\mathrm{RMSE}=\sqrt{\frac{\sum_{i}^{n}\left(O_{i}-P_{i}\right)^{2}}{n-1}}$

式中,$ \bar{x}$为总样本的均值, K是样本的数量。公式(6)和(7)中Oi表示实测数值, Pi表示模拟数值, 表示实测数值的均值, $\bar{P}$表示模拟数值的均值。

1.3 数据处理

利用Microsoft Excel 2016软件建立数据库, Origin pro 2022软件绘制图表和数值化。

2 结果与分析

2.1 区域尺度小麦产量和N2O排放效应模型的构建

2.1.1 N2O排放与模型输入参数之间的关系

从表1发现, 长江中下游搜集的数据中pH、土壤有机质(Soil organic matter, SOM)、年降雨量(LT_ Prec)、土壤总氮(Total nitrogen, TN)和N2O排放量(Cum N2O)的CV值均较大, 其中N2O排放量的CV值达到81.82%。这些结果表明数据可以代表实际生产场景中的大多数可能情况, 因此数据可用于分析更通用的小麦N2O排放情况。通过皮尔逊相关分析, 从相关系数可以看出, 在区域尺度上, N2O排放量与施氮量(N rate)、施肥次数(Split N)和年平均温度(LT_Temp)呈显著正相关, 相关系数在0.16~0.23之间, 随着施氮量的增加, 麦田引起的N2O排放越多。其中pH和土壤有机质(SOM)与N2O排放量呈现负相关, 相关系数分别为-0.20和-0.29。然而年降雨量(LT_Prec)与N2O排放量之间的相关性并不明显, 这可能与年降雨量与当季降雨量之间存在误差, 增加了对N2O排放的响应的不确定性。在长江中下游地区中, 秸秆还田量对的小麦N2O排放量会有一定的抑制作用, 但并不显著(图1)。

表1   田间管理措施、生态因子与N2O排放量的描述统计

Table 1  Descriptive statistics of field management practices, ecological factors and N2O emission

指标
Indicator		最小值
Min.		最大值
Max.		平均值
Mean		标准偏差
SD		标准差系数
CV (%)
N rate0300181.8982.2945.24
Split N132.630.5019.19
Straw rate08.251.452.40164.83
pH5.888.096.790.649.43
SOM (g kg-1)11.0057.9020.427.7838.10
TN (g kg-1)0.512.901.530.4630.06
LT_Temp (℃)15.1019.9515.960.462.89
LT_Prec (mm)750.001890.301117.32174.3115.60
Cum_N2O (kg hm-2)0.137.742.421.9881.82

SOM: 土壤有机质; TN: 土壤总氮; LT_Temp: 年平均温度; LT_Prec: 年平均降雨量; Cum_N2O: N2O排放量。

SOM: soil organic matter; TN: total nitrogen; LT_Temp: the annual average temperature; LT_Prec: the annual average precipitation; Cum_N2O: cumulative N2O emission.

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图1

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图1   田间管理措施、生态因子与N2O排放量之间的皮尔逊相关性热图

缩写同表1。

Fig. 1   Pearson correlation heatmap of field management practices, ecological factors and N2O emission

Abbreviations are the same as these given in Table 1.


2.1.2 产量和N2O排放效应模型构建与验证

基于收集的文献数据和RF方法, 将数据集根据无秸秆投入(S=0)、小于4.50 t hm-2的秸秆投入(0<S<4.5)和大于4.50 t hm-2的秸秆投入(S>4.50)进行划分, 构建了小麦产量和N2O排放效应模型。结果表明, RF模型可以预测不同区域氮肥和秸秆投入下的小麦产量和累积N2O排放量。小麦产量模型训练和测试集R2介于0.66~0.96, RMSE介于0.35~0.70 t hm-2, 验证结果达到显著水平(P< 0.001)。产量在各秸秆投入下差异不大。对于温室气体而言, N2O模型训练和测试集R2介于0.65~ 0.85, RMSE介于0.81~1.11 kg hm-2, 验证结果达到显著水平(P<0.001), N2O累积排放随着秸秆投入的增加而增加(图2)。

图2

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图2   N2O排放和小麦产量模型的训练与验证结果

Fig. 2   Calibrations and validations of N2O emission and wheat yield RF models


2.1.3 特征重要性分析

本研究中小麦产量和N2O排放效应模型分别考虑不同的自变量, 不同自变量对模型重要性的量化结果如图3所示。经过模型选择, 对应于产量模型, N rate对于产量- RF模型的平均不纯度减少最多, 达到0.43, 而SOM、LT_Prec、LT_Temp对于产量-RF模型的平均不纯度减少达到0.12~0.14, 在区域尺度上N rate、SOM、LT_Prec、LT_Temp和土壤pH是影响产量形成的最重要变量。对于N2O模型, SOM、N rate和TN对于N2O-RF模型的平均不纯度减少达到0.17~0.22, 而LT_Prec、LT_Temp在N2O-RF模型的贡献度较低, 并不能区分作物种植的季节性, 很大程度上弱化了不同种植季节之间的温度和降水量差异。

图3

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图3   各个模型中的特征重要性

a: 一氧化二氮; b: 产量。缩写同表1。

Fig. 3   Independent importance of each RF model

a, N2O; b, yield. Abbreviations are the same as those given in Table 1.


2.2 不同目标下的氮肥和秸秆投入措施推荐

2.2.1 不同情景下的小麦产量和N2O排放量模拟及函数构建

通过在如皋的田间试验中获取实测数据(SOM=13.34 g kg-1、TN=1.5 g kg-1、Split N=2次、LT_Prec =1145.28 mm、LT_Temp =17.49℃、pH 8.2), 并设置秸秆投入水平为0、2.25、4.50和6.75 t hm-2和氮肥投入水平为0到300 kg hm-2作为情景参数, 基于所构建的产量和N2O效应模型对该地不同情景下的小麦产量和N2O累积排放量进行估算, 并对反演的产量采用一元二次方程进行拟合以及对反演的N2O累积排放量采用线性方程和指数方程进行拟合, 拟合方程如表2所示, 其中N2O排放与施氮量之间的方程的斜率随秸秆投入先增加后减少, 表现秸秆的增加可能会对矿化氮进行固持, 减少了N2O排放生成的底物。而产量函数中负值的系数随着秸秆投入而降低, 秸秆投入在较低氮肥投入下会抑制小麦的前期生长从而抑制了产量形成, 而第二个系数为正值, 则随着秸秆投入的增加而逐渐递增, 表现为随施肥量增加, 可以有效地满足小麦生长所需要的氮素营养, 从而形成合理的群体保证产量, 并且随着施肥量的增加, 秸秆后期的养分效益逐渐显现出来, 表现出函数在较大的氮肥投入下产量会表现为秸秆投入高于无秸秆投入, 并且一定程度上秸秆的养分可以替代化学肥料的养分用于生产。

表2   不同秸秆还田量情景的N2O累积排放和小麦产量模拟模型

Table 2  Simulation model of cumulative N2O emission and wheat yield under different scenarios of straw returning to the field

处理
Treatment		N2O累积排放量Cumulative N2O emissions (kg hm-2)产量
Yield (t hm-2)
LINEAREXPONENT
S0y = 0.0077x+1.0226y = 1.1281e0.004xy = -0.1111x2+47.077x+1506.0
S2.25y = 0.0078x+1.1241y = 1.2180e0.0038xy = -0.1117x2+46.961x+1526.2
S4.50y = 0.0071x+1.2914y = 1.3763e0.0033xy = -0.1197x2+49.842x+1489.9
S6.75y = 0.0069x+1.2907y = 1.3727e0.0033xy = -0.1205x2+50.221x+1491.4

S0、S2.25、S4.50、S6.75分别代表秸秆投入水平为0、2.25、4.50和6.75 t hm-2。

S0, S2.25, S4.50, and S6.75 represent the straw input levels of 0, 2.25, 4.50, and 6.75 t hm-2, respectively.

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2.2.2 面向不同目标下的氮肥和秸秆投入推荐及效益评估

基于表2的模型计算出不同秸秆还田量和氮肥投入下的CEE和NEEB如图4所示, 对于N2O排放随氮肥投入无论是呈现线性增长还是指数增长, 在不同的秸秆投入下, 小麦的CEE变化均表现为随氮肥量增加而呈现先降低后升高的趋势, 在CEE评估下, 不同秸秆投入下施用氮肥以113~130 kg hm-2为边界线, 低于该氮肥投入水平N2O排放的增加会低于产量的增加, 直到CEE的边界施氮量, 后面继续增加施肥, 产量的增长速度是低于N2O排放量的增长的, 表现为N2O排放-氮肥投入呈现凹形的增长曲线, 而产量-氮肥投入呈现凸性的增长曲线。而NEEB则表现为先升高后下降的趋势, 这体现出将环境影响通过计算环境经济成本的形式进行考虑, 增加氮肥投入所增加的产量收益是远大于其带来的环境成本, 因此以NEEB最佳为目标下推荐氮肥用量更侧重于保证作物产量的稳定, 同时减少了为追求产量最高下施肥所引起的环境影响。从表3可以看到, 观察到在不同秸秆投入下产量最高、CEE最低、NEEB最高下的氮肥施用量以及经济收益。在0~6.75 t hm-2的秸秆投入下, 达到产量最高所需的氮肥量为208~212 kg hm-2, 并且随秸秆投入的增加, 达到产量最高所需的氮肥量有所下降。在不同的秸秆投入中, 小麦达到CEE最低所需氮肥量在113~130 kg hm-2, 其净生态收益也是最少的, 而达到最高NEEB所需氮肥则在202~205 kg hm-2, 其中6.75 t hm-2的秸秆投入下施用202 kg hm-2的氮肥可以获得最高的生态收益13,669.18元。

图4

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图4   不同氮肥和秸秆投入下的CEE和NEEB响应

处理同表2。

Fig. 4   Response of CEE and NEEB to different N and straw application

Treatments are the same as those given in Table 2.


表3   不同目标下小麦施氮量和秸秆量投入及其净生态环境效益

Table 3  Nitrogen and straw inputs of wheat under different targets and their net ecological and environmental benefits

模型
Model		秸秆量
Straw rate (t hm-2)		施氮量
N rate (kg hm-2)		收益
Benefit (CHY)
Yield-max021212,816.00
2.2521012,851.00
4.5020813,453.00
6.7520813,657.88
CEE-LINEAR011310,702.22
2.2511610,934.81
4.5012912,029.94
6.7513012,255.24
CEE-EXPONENT011410,750.49
2.2511710,981.24
4.5012711,951.29
6.7512712,135.69
NEEB-LINEAR020512,829.70
2.2520312,863.29
4.5020113,464.20
6.7520213,669.00
NEEB-EXPONENT020512,830.40
2.2520312,864.52
4.5020113,464.99
6.7520213,669.18

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3 讨论

3.1 麦田N2O排放的主要驱动因素

麦田的N2O排放受到管理措施(施氮量、秸秆还田、施肥次数)的影响。本研究中, 氮肥施用与N2O排放通量呈现正相关, 同时也是N2O-RF模型中的主要贡献因子。其次, 氮肥的分次施用通常被认为比一次性施用排放更低[55]。但本研究中Split N与N2O排放量呈现正相关关系, 与前人的研究有点差异, 可能是由于在长江中下游地区多次施肥容易碰上降雨, 从而激发更高的N2O排放。此外, 秸秆的掺入和表面覆盖在长期试验中极大地影响了N2O排放量[56]。前人研究也表明, 对于土壤N2O排放, 秸秆还田具有抑制作用[57]、促进[58]或无显著的影响效果[59]。本研究结果表明, 秸秆还田量与N2O排放的相关分析呈现负相关, 这些不一致的发现可能归因于秸秆还田量和土壤类型的差异[50]。本研究的土壤N2O排放量在构建的指数模型中也发现了随着秸秆投入呈现先增加后减少的趋势(表2)。施氮肥和秸秆还田都可能会刺激N2O排放[60]。此外, 本研究中基于Pearson相关分析发现, N2O排放受到了年平均温度的显著影响, 较高的温度通常会通过反硝化作用增加农田生态系统中土壤N2O的排放量[61]。前人研究表明, SOM和土壤N含量也是影响土壤N2O排放变化的重要因素[62]。在考虑土壤N含量的作用时, pH对于N2O排放量的预测并没有具有较高的特征重要性, 这与前人的研究是一致的[63]。本研究揭示了土壤N2O排放的控制因素是复杂的, 虽然气候因素(如年平均气温、年降雨量)、土壤理化性质(如土壤pH值、SOM)显着影响土壤N2O排放, 但作为硝化和反硝化作用底物的土壤N含量(土壤总氮), 在土壤N2O排放中也起到关键作用。

3.2 RF预测产量和N2O排放的可行性

了解和量化中国长江中下游流域的产量和N2O排放量对于保证产量和减少环境污染至关重要。由于区域内气候、土壤和管理制度的变异性, 综合探究不同氮肥和秸秆投入的响应是复杂的。最近, 机器学习技术已成功应用于农业生产, 以研究各种农艺指标[18]。作为一种流行的基于决策树的集成机器学习算法, RF可以处理变量之间的非线性效应和复杂的相互作用。通过RF模型的模拟, N2O排放和产量的估算是基于数据驱动的, 而不依赖于预先指定的方程或函数形式。在这里, 我们将有关天气、管理和土壤状况的大型数据集与RF算法相结合, 以识别中国长江中下游流域的稻麦轮作区的N2O排放量和产量。对于N2O排放来说, 使用线性模型仅靠基础土壤数据和施氮量无法准确预测, 而RF模型对稻田下N2O总排放量的预测是显著优于其他模型的[64]。RF模型也已被用于预测玉米田的N2O排放和N淋溶[65]。而对于产量来说, 大量Meta分析的研究已经表明土壤理化性质, 人为管理措施以及氮肥和秸秆使用量是主导产量变异的主要因素[55,66]。因此, 本研究中使用RF模型模拟不同氮肥和秸秆投入下麦田的产量和N2O总排放量是可行的, 该模型考虑了变量的非线性响应, 获取优异的性能(R2 = 0.65~0.96)。在本研究中, 模型模拟由于忽略了几个因素而存在一些局限性。在这里, 我们搜集的数据中多是来自田间试验数据, 大多是基于小麦的基本苗在225万公顷进行试验, 从而简化了现实生产中的密度响应, 这意味着改进氮肥和秸秆的投入并不是孤立地进行, 而是要与其他管理措施相结合, 例如增加密度, 以扩大氮肥和秸秆投入的养分利用率。其次是需要通过考虑本研究无法解决的其他可能因素(例如倒伏、病虫害风险)来改进N2O排放和产量预测。此外, 我们的N2O排放和产量模型是使用RF算法开发的, 但仍可能存在一些不足, 进一步可以通过结合更多生物物理因子和机器学习建模来改进N2O排放和产量的评估。尽管存在这些限制, 模型的良好性能表明, 气候-土壤数据集与机器学习技术相结合是研究影响N2O排放和产量变化的有效方法。

3.3 麦田优化施肥方案推荐

化肥施用不当导致养分失衡、利用效率低下, 对环境造成大量损失, 已成为我国小麦生产系统的普遍现象。然而, 确定合适的施肥量仍然是基于科学的养分管理的基础。以往农民通常根据当地田间试验或过去的经验使用固定的氮肥施用量, 较容易造成环境污染[67]。

早期的施肥研究是基于粮食产量目标出发, 基于经验分析的方法对产量/投入之间的关系进行定量, 从而推荐施肥策略, 相较于农户方案降低了氮肥施用量并提高作物产量, 但忽略了环境问题[68⇓-70]。在考虑环境成本方面, 前人研究通过预测油菜籽产量、种植者的利润和EONR值, 从而为加拿大东部的油菜籽生产提供环境经济最佳的N推荐[19]。而优化施氮量并不能完全满足最佳氮肥管理, 一次性撒肥、施肥不足、施肥过量、秸秆不合理使用等传统田间管理方式在中国许多地区的小农户中仍然存在。在本研究中, 通过结合特定地点的情景设置反演数据构建的产量一元二次模型发现, 随氮肥投入增加, 产量呈现先增后减趋势。当秸秆还田量大于4.5 t hm-2时, 本研究构建的产量模型反演的产量高于无秸秆投入处理, 因此秸秆还田可以是提高作物产量的有效措施之一, 但同时会受到氮肥配施量的影响与调控, 通过改善管理实践是实现以更少的投入生产稳定的粮食和减少温室气体排放的重要策略。

目前追求优化策略组合以提高粮食产量和降低环境成本, 仍然缺乏对不同目标下的施肥措施策略组合进行综合的评估。本研究中, 选用碳排放强度作为排放最低下的生态环境效益的定量评估, 而NEEB则结合作物产量、农业活动和全球变暖潜能值的成本[50], 进而考虑生态环境效益。经济效益通常是农民改善农艺管理和政策制定者提出有效农业政策的主要动力, 本研究表明, 在不同秸秆投入下达到CEE最低, 会随着秸秆投入的增加氮肥施用也有所增加, 并获得更高的NEEB。这可能与CEE评估下的施肥推荐中, 为保证排放最低的前提是以牺牲产量为代价的。而相较于CEE评估, NEEB评估有助于以货币为基础的方式让农民更容易评估科学决策, 并鼓励他们采用环境友好型管理。与低秸秆投入相比, 在较高的秸秆投入下减少了氮肥施用并获得了更高的NEEB, 结果有利于鼓励农民采用秸秆还田技术, 有益于人们减少购买氮肥的成本并有助于减少人们焚烧秸秆的行为。总而言之, 秸秆还田在实际农业生产中具备固碳减排的潜力, 配施合适的氮肥可以获得更好的NEEB, 前人研究也与本试验的结果相似[16]。

4 结论

在稻麦复种模式下, 麦田N2O排放因秸秆投入和施肥不同存在显著差异, 表现为N2O的累积排放量随施氮量和秸秆还田量的增加而显著增加。在区域尺度上, 通过搜集长江中下游地区上氮肥和秸秆投入对小麦产量与N2O排放的相关文献数据, 并建立了RF效应模型, 验证结果表明模型拟合情况良好, 结果表明氮肥和秸秆投入的小麦产量和N2O排放会受到人为管理因素、土壤因素和气候因素的影响。基于开发的模型结合情景设置进行了试验地点的小麦产量和N2O排放模拟, 并评估了碳排放强度和净生态环境经济效益。若追求高产, 所需氮肥量为208~212 kg hm-2, 若以达到最小碳排放为目标, 所需氮肥量在113~130 kg hm-2, 若要实现最大生态经济效益, 所需氮肥则是在202~205 kg hm-2, 其中在6.75 t hm-2的秸秆投入下施用202 kg hm-2的氮肥可以获得最高的生态收益13,669.18元。优化氮肥和秸秆投入能在顺应绿色生产的前提下收获理想的经济效益, 未来应用前景广阔。

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. The effects of nitrogen and straw management on global warming potential (GWP) and greenhouse gas intensity (GHGI) in a winter wheat–summer maize double-cropping system on the North China Plain were investigated. We measured nitrous oxide (N2O) emissions and studied net GWP (NGWP) and GHGI by calculating the net exchange of CO2 equivalent (CO2-eq) from greenhouse gas emissions, agricultural inputs and management practices, as well as changes in soil organic carbon (SOC), based on a long-term field experiment established in 2006. The field experiment includes six treatments with three fertilizer N levels (zero N (control), optimum and conventional N) and straw removal (i.e. N0, Nopt and Ncon) or return (i.e. SN0, SNopt and SNcon). Optimum N management (Nopt, SNopt) saved roughly half of the fertilizer N compared to conventional agricultural practice (Ncon, SNcon), with no significant effect on grain yields. Annual mean N2O emissions reached 3.90 kg N2O-N ha−1 in Ncon and SNcon, and N2O emissions were reduced by 46.9% by optimizing N management of Nopt and SNopt. Straw return increased annual mean N2O emissions by 27.9%. Annual SOC sequestration was 0.40–1.44 Mg C ha−1 yr−1 in plots with N application and/or straw return. Compared to the conventional N treatments the optimum N treatments reduced NGWP by 51%, comprising 25% from decreasing N2O emissions and 75% from reducing N fertilizer application rates. Straw return treatments reduced NGWP by 30% compared to no straw return because the GWP from increments of SOC offset the GWP from higher emissions of N2O, N fertilizer and fuel after straw return. The GHGI trends from the different nitrogen and straw management practices were similar to the NGWP. In conclusion, optimum N and straw return significantly reduced NGWP and GHGI and concomitantly achieved relatively high grain yields in this important winter wheat–summer maize double-cropping system.\n

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Soil sustainability is based on soil microbial communities’ abundance and composition. Straw returning (SR) and nitrogen (N) fertilization influence soil fertility, enzyme activities, and the soil microbial community and structure. However, it remains unclear due to heterogeneous composition and varying decomposition rates of added straw. Therefore, the current study aimed to determine the effect of SR and N fertilizer application on soil organic carbon (SOC), total nitrogen (TN), urease (S-UE) activity, sucrase (S-SC) activity, cellulose (S-CL) activity, and bacterial, fungal, and nematode community composition from March to December 2020 at Guangxi University, China. Treatments included two planting patterns, that is, SR and traditional planting (TP) and six N fertilizer with 0, 100, 150, 200, 250, and 300 kg N ha–1. Straw returning significantly increased soil fertility, enzymatic activities, community diversity, and composition of bacterial and fungal communities compared to TP. Nitrogen fertilizer application increased soil fertility and enzymes and decreased the richness of bacterial and fungal communities. In SR added plots, the dominated bacterial phyla were Proteobacteria, Acidobacterioia, Nitrospirae, Chloroflexi, and Actinobacteriota; whereas fungal phyla were Ascomycota and Mortierellomycota and nematode genera were Pratylenchus and Acrobeloides. Co-occurrence network and redundancy analysis (RDA) showed that TN, SOC, and S-SC were closely correlated with bacterial community composition. It was concluded that the continuous SR and N fertilizer improved soil fertility and improved soil bacterial, fungal, and nematode community composition.

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Rasouli, S., Whalen, J. K. and Madramootoo, C. A. 2014. Review: Reducing residual soil nitrogen losses from agroecosystems for surface water protection in Quebec and Ontario, Canada: Best management practices, policies and perspectives. Can. J. Soil Sci. 94: 109–127. Eutrophication and cyanobacteria blooms, a growing problem in many of Quebec and Ontario's lakes and rivers, are largely attributed to the phosphorus (P) and nitrogen (N) emanating from intensively cropped agricultural fields. In fact, 49% of N loading in surface waters comes from runoff and leaching from fertilized soils and livestock operations. The residual soil nitrogen (RSN), which remains in soil at the end of the growing season, contains soluble and particulate forms of N that are prone to being transported from agricultural fields to waterways. Policies and best management practices (BMPs) to regulate manure storage and restrict fertilizer and manure spreading can help in reducing N losses from agroecosystems. However, reduction of RSN also requires an understanding of the complex interactions between climate, soil type, topography, hydrology and cropping systems. Reducing N losses from agroecosystems can be achieved through careful accounting for all N inputs (e.g., N credits for legumes and manure inputs) in nutrient management plans, including those applied in previous years, as well as the strategic implementation of multiple BMPs and calibrated soil N testing for crops with high N requirements. We conclude that increasing farmer awareness and motivation to implement BMPs will be important in reducing RSN. Programs to promote communication between farmers and researchers, crop advisors and provincial ministries of agriculture and the environment are recommended.

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作物学报, 2023, 49: 3100-3109.

DOI      [本文引用: 1]

探究施氮量和播种量互作对冬小麦产量、生长发育和生态场特性的影响, 利用生态场理论揭示不同小麦群体竞争力差异及其与产量的关系, 明确冬小麦适宜的氮肥用量和播种量, 为冬小麦高产高效生产提供依据。2020年10月至2022年6月于河南省温县设置冬小麦氮肥用量和播种量双因素交互田间试验, 研究了施氮量(0、90、180、270、360 kg N hm<sup>-2</sup>)和播种量(135、180、225、270 kg hm<sup>-2</sup>)对冬小麦籽粒产量、氮积累量等的影响, 测定小麦株高、冠幅和单株分蘖等生长发育指标, 计算个体生态势和群体生态场并分析其与产量间关系。结果表明, 两年取得最高产量的播种量均为225 kg hm<sup>-2</sup>, 施氮量分别为270 kg hm<sup>-2</sup>和180 kg hm<sup>-2</sup>, 较其他处理平均增产7.5%和18.1%; 施氮后小麦氮积累量提高57.3%, 生态势提高72.7%; 提高播种量后群体茎蘖数提高34.7%, 单株小麦发育水平下降, 生态势下降11.4%。施氮量和播种量通过共同影响株高和冠幅影响生态势影响距离, 其他处理较135 kg hm<sup>-2</sup>播种量不施氮处理影响距离提高23.0%。冬小麦群体生态场面积与产量呈一元二次函数关系, 施氮和提高播种量, 冬小麦群体生态场面积分别提高116.7%和52.5%。本试验条件下, 通过氮肥用量和播种量调控冬小麦群体发育质量, 控制群体竞争力, 构建了理想群体, 实现了冬小麦高产与高效生产; 冬小麦氮密优化组合施氮量239.8 kg hm<sup>-2</sup>、播种量228.7 kg hm<sup>-2</sup>, 具有适宜的生态场和理想群体, 产量较高, 可在豫北地区推广应用。

Zhou Q, Li L T, Zhang L L, Miao Y H, Wang Y L.

Effects of interaction of nitrogen level and sowing rate on yield, growth, and ecological field characteristics of winter wheat

Acta Agron Sin, 2023, 49: 3100-3109 (in Chinese with English abstract).

DOI      [本文引用: 1]

The objective of this study is to study the effects of different nitrogen level and sowing rate on the yield, growth and development, and ecological field characteristics of winter wheat, to explore the relationships between the wheat population competitiveness and its yield based on the ecological field theory, and to find a balance between nitrogen level and sowing dates for high yield and high efficiency in winter wheat. Field experiments were established as a split-plot design of five nitrogen levels (0, 90, 180, 270, and 360 kg hm-2) and four sowing rates (135, 180, 225, and 270 kg hm-2) from 2020 to 2022 at Wen County, Henan Province. The grain yield, nitrogen accumulation, growth and development indexes (i.e., plant height, crown width, and tillering) were measured and calculated for the aforementioned treatments. Results showed that the optimal sowing rate were both 225 kg hm-2 for the two growing seasons, and the correspondingly nitrogen rates were 270 kg hm-2 and 180 kg hm-2, respectively, which achieving the highest grain yield. Compared to the other treatments, the yield increased by 7.5% and 18.1% with the optimal nitrogen and sowing rate treatment combinations. Moreover, nitrogen accumulation increased by 57.3% for nitrogen application treatments, and the potential energy of growth was increased by 72.7%. However, the tillering increased by 34.7%, and the development level per plant decreased with the sowing rates increased, the potential energy of growth decreased by 11.4%. Plant height and crown width were also significantly influenced by nitrogen level and the sowing rate. Compared to the 135 kg hm-2 sowing rate and without nitrogen, the scope of ecological field increased by 23.0% in the other treatments. The relationship between winter wheat population ecological field area and yield was a quadratic function. When nitrogen was applied and seeding rate was increased, the population ecological field area of winter wheat increased by 116.7% and 52.5%, respectively. The appropriate N level and sowing rate in winter wheat for improved growth, yield, and ecological field characteristics in the experimental area was 239.8 kg N hm-2 and 228.7 kg hm-2, respectively, which could be extended to the application in the northern of Henan.

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