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秸秆还田对我国主要粮食作物产量效应的整合(Meta)分析

本站小编 Free考研考试/2021-12-26

杨竣皓,, 骆永丽, 陈金, 金敏, 王振林, 李勇,山东农业大学/作物生物学国家重点实验室,山东泰安 271018

Effects of Main Food Yield Under Straw Return in China: A Meta-Analysis

YANG JunHao,, LUO YongLi, CHEN Jin, JIN Min, WANG ZhenLin, LI Yong,Shandong Agricultural University/State Key Laboratory of Crop Biology, Tai’an 271018, Shandong

通讯作者: 李勇,E-mail:woooowo@126.com

责任编辑: 杨鑫浩
收稿日期:2020-05-14接受日期:2020-07-30网络出版日期:2020-11-01
基金资助:国家重点研发计划项目.2017YFD0301001
国家重点研发计划项目.2016YFD0300400


Received:2020-05-14Accepted:2020-07-30Online:2020-11-01
作者简介 About authors
杨竣皓,E-mail:1023932708@qq.com








摘要
【目的】 定量分析全国范围内秸秆还田对3种粮食作物的产量效应,为秸秆还田技术的大面积推广应用提供科学依据。【方法】 本研究搜集了公开发表的相关文献274篇(截至2019年12月31日),整理了1 930组秸秆还田处理与秸秆不还田处理的农作物产量数据。运用整合分析(meta-analysis)方法(主要分析过程包括计算效应值、异质性检验、meta亚组分析、发表偏倚性检验),明确了秸秆还田对3种粮食作物产量的综合效应,进而量化分析了试验区域、年均气温、年均降雨量、土壤质地、土壤酸碱性、作物种类、种植制度、耕作方式、施肥模式、还田年限及秸秆还田量对秸秆还田增产效应的影响。【结果】 与秸秆不还田相比,秸秆还田显著提高了3种粮食作物产量,平均增产率约为8.06%(95%CI:7.52%—8.60%),分析结果不存在发表偏倚。东南地区秸秆还田增产率最高,达到9.37%(95%CI:8.11%—10.64%);年平均气温为5—10℃、年平均降雨量超过1 200 mm时,秸秆还田的增产效应较高;在黏土、壤土及砂土3种土壤质地中,秸秆还田的增产率分别为8.13%(95%CI:6.80%—9.49%)、9.04%(95%CI:8.06%—10.01%)、6.96%(95%CI:5.18%—8.77%);在弱酸性(pH<6.5)土壤中,秸秆还田的增产率较高。在小麦、玉米、水稻3种作物中,秸秆还田对玉米的增产率达到9.22%(95%CI:8.38%—10.05%);翻耕与免耕是最能发挥秸秆还田增产效应的耕作方式,增产率分别为11.05%(95%CI:10.05%—12.05%)、8.98%(95%CI:7.21%—10.79%)。不施肥时,秸秆还田显著提高农作物产量,增产率达到25.66%(95%CI:22.04%—29.38%),增产率显著高于正常施肥时的8.08%(95%CI:7.50%—8.68%),但整体的产量水平较低。秸秆还田的增产率达到9.56%(95%CI:8.21%—10.93%),且还田年限超过20年时增产效应显著提高(增产率为15.42%,95%CI:11.05%—19.95%)。此外,最适宜的秸秆还田量为半量还田(增产率为9.09%,95%CI:7.41%—10.79%)。【结论】 秸秆还田能够显著提高粮食作物产量,在不同的农业生产区以免耕或翻耕作业配合正常的施肥模式、适宜的秸秆还田量长期还田能够保持农作物的持续增产。
关键词: 秸秆还田;全国;农作物;产量效应;整合分析

Abstract
【Objective】In order to provide scientific grounds for the implementation of grain crop straw return, this study quantified the yield effect of straw return.【Method】It was collected and sorted out the published Chinese literatures in the past 30 years (a total of 274 piece of literature and 1 930 pairs data until December 31, 2019). By using meta-analysis method, the comprehensive effect of straw returning on crop yield was clarified with the main analysis process, included calculation of effect value, heterogeneity test, meta-subgroup analysis and publication bias test. And then, the effects under different experiment region, average annual temperature, average annual precipitation, soil texture, soil pH, crop type, planting system, tillage method, fertilization method, experiment duration and return amount was further quantitatively analyzed.【Result】Compared with straw remove, straw return significantly increased crop yield, and the average increasing rate was about 8.06%, with a 95% confidence interval of 7.52%-8.60%. No publication bias was found in the result. The yield effect was the highest in the southeastern region, reaching 9.37% (95% CI: 8.11%-10.64%). The straw-return effect was higher when the average annual temperature was 5-10 °C and the average annual precipitation is more than 1 200 mm. In different soil texture, the yield effect of straw return was 8.13% in clay (95% CI: 6.80%-9.49%), 9.04% in loam (95% CI: 8.06%-10.01%) and 6.96% in sandy soils (95% CI: 5.18%-8.77%), respectively. Among the three types of grain crops, namely, wheat, corn, and rice, the increase rate of yield on maize reached 9.22% (95% CI: 8.38%-10.05%) by straw returning. Plowing and no-till was the best tillage methods exerting the yield effect of straw returning, the increasing rate of yield were 11.05% (95% CI: 10.05%-12.05%) and 8.98% (95% CI: 7.21%-10.79%), respectively. When the straw was returned to the field without fertilization, the crop yield was significantly increased with an increase rate of 25.66% (95% CI: 22.04%-29.38%), which was significantly higher than that of 8.08% (95% CI: 7.50%-8.68%) under normal fertilization, but the overall yield level was lower. The yield increase rate of straw mulching reached 9.56% and the yield increase effect of straw mulching over 20 years was significantly increased (yield increase rate: 15.42%, 95% CI: 11.05%-19.95%). In addition, the most suitable amount of straw was half of the ex-crop (increase rate of yield was 9.09%, 95% CI: 7.41%-10.79%).【Conclusion】Straw return could significantly increase crop yield in different agricultural production areas. Furthermore, the long-term implication of crop straw with no-till or plowing tillage, normal fertilization and appropriate amount, could maintain continuous increase in crop yield.
Keywords:straw return;China;crop;yield effect;meta-analysis


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本文引用格式
杨竣皓, 骆永丽, 陈金, 金敏, 王振林, 李勇. 秸秆还田对我国主要粮食作物产量效应的整合(Meta)分析[J]. 中国农业科学, 2020, 53(21): 4415-4429 doi:10.3864/j.issn.0578-1752.2020.21.010
YANG JunHao, LUO YongLi, CHEN Jin, JIN Min, WANG ZhenLin, LI Yong. Effects of Main Food Yield Under Straw Return in China: A Meta-Analysis[J]. Scientia Acricultura Sinica, 2020, 53(21): 4415-4429 doi:10.3864/j.issn.0578-1752.2020.21.010


0 引言

【研究意义】农作物秸秆是农业生产的主要产物之一,含有丰富的可供农作物生长的氮、磷、钾养分[1]。作为传统的农业大国,中国每年产生的农作物秸秆种类丰富,各类农作物秸秆的总产量约占世界秸秆产量的25%[2],同时,农作物秸秆的利用方式单一、利用效率低的问题长期存在[3]。秸秆还田是兼顾了经济效益与环境效益的利用方式。近年来,随着农作物秸秆资源数量持续增加以及对还田技术研究的不断深入,降低还田成本,实现秸秆还田持续增产增收效应的目标成为可能[4]。【前人研究进展】国内外****通过田间定位试验对秸秆还田的产量效应进行了研究,获得了大量的田间试验数据,涉及的农作物既包括玉米[5]、水稻[6]、小麦[7]等在我国大面积种植的粮食作物,也有棉花[8]、油菜[9]、花生[10]等经济作物。研究表明,农作物秸秆还田能够显著改善土壤物理结构,降低土壤容重、提高土壤孔隙度[11];显著提高土壤中的全氮、速效氮及磷、钾养分含量[12],构建合理的土壤微生物群落结构[13],具有显著的增产作用,也具有良好的环境效益[14]。整合分析(meta-analysis)定义为对同一主题下多项独立试验的研究结果进行综合的统计方法,它的起源最早可以追溯到20世纪初[15]。【本研究切入点】目前,国内外****关于秸秆还田对农作物的产量效应研究多是基于某种作物或某个地区进行的,研究结果仅对某种作物或某个地区的生产具有理论指导意义,对跨作物种类或农业生产区的产量效应尚不清楚。为了定量分析全国区域内秸秆还田对农作物的产量效应,需要整合全国范围内的独立试验数据,运用科学的分析方法进行系统综述。【拟解决的关键问题】本研究搜集整理了近30年国内外****公开发表的中文文献数据,运用整合分析方法,定量分析了全国区域内秸秆还田对农作物的产量效应,并进一步明确了不同区域、种植制度、耕作方式、施肥模式等因素对产量效应的影响程度,旨在为秸秆还田技术在全国范围内的合理推广应用提供科学的理论依据。

1 材料与方法

1.1 数据来源

以“秸秆还田、产量”为关键词,在China National Knowledge Infrastructure(CNKI)等主要中文文献数据库中检索2019年12月31日之前发表的田间对照试验论文(不包括室内试验、评价类、综述类、模型模拟类等文章及相关专业的硕博士学位毕业论文)。按以下标准对检索到的文献进行筛选[16]以获得满足meta-analysis要求的数据:(1)试验结果发表在最新版北大中文核心期刊中收录的综合类期刊,主要包括第四编自然科学中的生物科学综合类及植物学类、第六编农业科学中的综合性农业科学类、农业基础科学类、农业工程类及农学农作物类和第七编工业技术中的环境科学类;(2)田间对照试验地位于中国大陆地区;(3)试验以我国主要农作物为研究对象,田间试验为包括秸秆还田和秸秆不还田处理的对照试验;(4)文献中有明确的试验重复数、各试验处理的产量均值;(5)相同的试验设计获得的试验数据发表在不同期刊时,选择相关信息描述最为详细的一篇文献;(6)补充原文参考文献中引用但未被检索到的遗漏文献。经筛选,共获得274篇相关文献。

提取文章中秸秆还田与秸秆不还田处理的产量、产量标准差、处理重复数及其他相关信息(试验区域、种植制度、耕作方式、施肥模式等),文字、表格形式展示的数据直接提取,图形形式展示的数据使用WebPlotDigitizer软件[17]提取,补全文章中未说明的各处理产量标准差[18,19],共得到1 930组试验数据用于Meta分析。

1.2 数据分类

考虑到秸秆还田的产量效应可能受其他相关因素影响,根据文献中提取到的相关试验信息进行归纳分组,整理得到以下影响因素(表1):试验区域[20]、年平均气温、年平均降雨量、土壤质地[21]、土壤酸碱性、作物种类、种植制度、耕作方式、施肥模式[22,23,24,25,26]、还田年限及秸秆还田量[27,28,29]。通过Meta亚组分析以考察各影响因素对秸秆还田的产量效应的影响程度[30],并寻找异质性来源。

Table 1
表1
表1试验相关数据分类
Table 1Classification of experiment data
影响因素 Influence factor分类亚组 Classification of sup-group
试验区域 Experiment region东北、华北、西北、东南、西南 Northeast, North, Northwest, Southeast, Southwest
年平均气温 Average annual temperature<5℃、5-10℃、10-15℃、15-20℃
年平均降雨量 Average annual precipitation<400 mm、400-800 mm、800-1200 mm、>1200 mm
土壤质地 Soil texture黏土、壤土、砂土 Clayey soil, loam soil, sand soil
土壤酸碱性 Soil pH弱酸性土壤、中性土壤、碱性土壤 pH<6.5、6.5<pH<7.5、pH>7.5
作物种类 Crop type小麦、玉米、水稻 Wheat, Maize, Rice
种植制度 Plant system一年一熟制、一年两熟制 Single plant, Double plant
耕作方式 Tillage method深耕、免耕、旋耕、翻耕 Deep tillage, No-till, Rotary tillage, Plowing
施肥模式 Fertilization不施肥(F0)、不施氮肥(N0)、不施钾肥(K0)、低氮施肥(N-)、高氮施肥(N+)、正常施肥(F)
还田年限 Experiment duration0-5、5-10、10-15、15-20、>20
秸秆还田量 Return amount少量还田(0-50%)、适量还田(50%-100%)、全量还田(100%)、过量还田(>100%)
F0、N0、K0、N-、N+、F分别为不施肥、不施氮肥、不施钾肥、低氮施肥、高氮施肥和正常施肥;0—50%、50%—100%、100%、>100%分别为以低于前茬作物秸秆产量的50%进行还田、以超过前茬作物秸秆产量的50%进行还田、以前茬作物全部秸秆产量进行还田和以超过前茬作物全部秸秆产量进行还田。下同
F0, N0, K0, N-, N+ and F represent different pattern of fertilization: no fertilization, no nitrogen, no potassium, low nitrogen, high nitrogen and normal fertilization. 0-50%, 50%-100%, 100% and >100% mean different amount of straw returned with less than 50% of the ex-crop straw, more than 50% of the ex-crop straw, all of the ex-crop straw and more than all of the ex-crop straw, respectively. The same as below

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1.3 Meta分析过程

1.3.1 计算效应值 首先,本研究采用随机效应模型评估秸秆还田对我国主要粮食作物的产量效应,选取生态学领域常用的反应比(R)的自然对数(lnR)作为Meta分析的效应值[31],来衡量秸秆还田对作物产量的影响。具体的计算公式如下:

lnR=ln(Xt/Xc)=ln Xt -ln Xc
式中,lnR为效应值,XtXc分别表示秸秆还田处理与秸秆不还田处理下作物的产量均值,单位为kg·hm-2

其次,Meta分析是对每项独立研究的效应值进行加权计算,得到总体平均效应值lnR++。计算时,需要确定每项独立研究的方差Vi、权重Wi,具体的公式如下:

$V_{i}=\frac{SD_{t}^{2}}{N_{t}X_{t}}+\frac{SD_{c}^{2}}{N_{c}X_{c}}$
$W_{i}=\frac{1}{(V_{i}+\tau^{2})}$
lnR++=∑(lnRi×Wi)/ ∑Wi
式中,SDt2与SDc2分别为秸秆还田与秸秆不还田处理下农作物的产量标准差,NtNc分别为秸秆还田与秸

秆不还田处理的试验重复数;τ2表示研究间方差[32]

总体平均效应值的标准差及其95%的置信区间通过以下公式计算:

$SE_{lnR_{++}}=\sqrt{1/\sum W_{i}}$
95%CI=lnR++±1.96SElnR++
若总体平均效应值的95%置信区间全部大于0,说明秸秆还田对农作物具有显著的增产作用;若全部小于0,说明秸秆还田对农作物具有显著的减产效应;若区间包含0,则说明秸秆还田对农作物无显著的产量效应。以上原则同样适用于Meta亚组分析。

最后,将总体平均效应值lnR++转换为总体的平均产量变化率RY,可以更直观地反映秸秆还田对农作物的产量效应[33],相应的转换公式为:RY=(elnR++-1)×100%。

1.3.2 异质性检验 秸秆还田的产量效应是否受其他相关因素影响可以通过异质性检验确定。总体平均效应值的异质性(Qt)表明数据偏离均值的程度,Qt值越大,数据的离散程度越大。数据的整体异质性包括由已知因素引起的异质性(Qm)和未知因素引起的异质性(Qe),已知因素即在文献中提取到的相关信息,作为Meta分析中的解释变量。如果异质性检验达到极显著程度(PQ-val<0.0001)[34,35],需要引入解释变量解释总体平均效应值的异质性来源[36]

1.3.3 Meta亚组分析 Meta亚组分析是为了进一步阐明不同影响因素下秸秆还田对农作物的产量效应,同样运用随机效应模型,计算方式与总体的平均效应值计算方式相同[37]

1.3.4 发表偏倚性检验 发表偏倚性问题是任何科学研究中的一个重要问题[38]。Meta分析是基于各项独立研究基础之上进行的系统性研究,因此Meta分析的使用更加注重发表偏倚性问题。发表偏倚性检验主要有两种方法:一种是漏斗图检验,根据漏斗图的对称性判断本研究是否具有发表偏倚性问题;另一种是计算失安全系数[39],失安全系数的阈值是(5n+10),其中n为Meta分析中的数据量,当失安全系数低于阈值时表明本研究存在发表偏倚性问题[40]

1.4 统计分析

本研究使用Microsoft Excel 2013记录文献数据,建立完整的秸秆还田产量数据库,并进行基本的统计计算。整合分析过程使用OpenMEE软件及R-Studio软件中的“metafor”软件包进行[41],OpenMEE是一款跨平台免费开放的生态学领域专业Meta分析软件[42],作图软件为Origin 9.1。

2 结果

2.1 秸秆还田对农作物产量的平均效应值及发表偏倚

采用随机效应模型计算秸秆还田对农作物产量的平均效应值(表2)。结果表明,秸秆还田能够显著提高农作物产量,平均增产率为8.1%(95%CI:7.5%—8.7%)。异质性检验Qt值达到显著水平(PQ-val<0.001),需要引入解释变量。发表偏倚性检验漏斗图(图1)的对称性达到显著水平(PB-val>0.05),表明本研究不存在发表偏倚。此外,本研究的失安全系数为19 338 930,其统计学意义为至少需要19 338 930组具有发表偏倚性的研究数据才能改变本研究的结论。

Table 2
表2
表2秸秆还田对农作物产量的平均效应值
Table 2Average effect size of crop yield under straw return
模型
Model
增产率
Rate (%)
置信区间CI (%)Z-valnQ-valPQ-valI2 (%)PB-val
下限LL上限UL
随机效应模型 REM8.067.528.6030.1931930674330.00097.550.0605
REM、CI、LL、UL分别表示随机效应模型、置信区间、下限及上限。Z为效应值检验的统计量;n为效应值数量;Q为异质性检验的统计量;PQ为异质性检验的显著程度;I2为研究间方差占总方差的比例;PB为发表偏倚性检验的显著程度
REM, CI, LL, UL represent random effects model, confidence interval, lower limit and upper limit, respectively. Z is the statistic value of effect size; n is the number of effect size; Q is the statistic value of heterogeneity; PQ is the significant value of heterogeneity; I2 is the percentage of variance between studys; PB is the significant value of publication bias

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图1

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图1发表偏倚性检验漏斗图

Fig. 1Funnel plot of publication bias test



2.2 秸秆还田对农作物产量效应的Meta亚组分析

通过Meta亚组分析对每组解释变量进行研究,结果表明秸秆还田对农作物产量的综合效应量受土壤酸碱性、作物种类、种植制度、耕作方式、施肥模式、还田方式等因素的影响达到极显著水平(PQ-val<0.0001)(表3)。所有解释变量的R2值,∑R2=27.71%,表明本研究纳入的解释变量能够解释27.71%的异质性来源,剩余的异质性来源有待进一步研究。

Table 3
表3
表3Meta亚组分析结果
Table 3Results of sub-group meta-analysis
解释变量 explaning varianceI2 (%)QmPQ-valR2 (%)
试验区域 Region97.597.10780.13030.23
年均气温 Average annual temperatur97.602.92580.40280.03
年均降水量 Average annual precipitation97.5810.48920.01480.51
土壤质地 Soil character97.606.01490.11090.12
土壤酸碱性 Soil pH97.5434.9845<0.00011.96
作物种类 Crop type97.22246.6566<0.000113.75
种植模式 Plant system97.606.58130.01030.39
耕作方式 Tillage method97.5159.3379<0.00013.04
肥料运筹 Fertilization97.44120.1434<0.00016.52
还田年限 Return duration97.5625.4725<0.00011.28
秸秆还田量 Straw amount97.601.91790.58960.00
I2为研究间方差在总方差中的比例;Qm为解释变量异质性检验的统计量;PQ-val为解释变量异质性检验的显著程度;R2为解释变量能够解释的异质性
I2 means the percentage of variance between studys; Qm means the statistic value of heterogeneity for explaining variance; PQ-val means the significant value of heterogeneity for explaining variance; R2 means the heterogeneity for explaining variance

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在不同的试验区域中(图2-a),东南地区秸秆还田的增产率最高,达到9.37%(95%CI:8.11%—10.64%),西南地区为8.34%(95%CI:6.33%—10.38%),华北地区为8.04%(95%CI:7.24%—8.85%),东北地区为7.77%(95%CI:6.13%—9.42%),西北地区为6.76%(95%CI:5.50%—8.05%),增产率最低。

图2

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图2秸秆还田对农作物产量效应的影响因素分析

Fig. 2Analysis of factors affecting crop yield under straw-return



秸秆还田的增产效应在年均气温达到5—10℃时的增产率((8.73±1.06)%)最高,在年均气温低于5℃时的增产率((7.65±3.04)%)最低(图2-b)。随着年均降水量的增加,秸秆还田的增产率呈现出先增加后降低的趋势(图2-c)。年均降水量低于400 mm时,秸秆还田对农作物的增产率((5.57±1.77)%)最低,年均降水量超过400 mm时,增产率显著提高。在年均降水量超过1 200 mm时,秸秆还田的增产率最高,达到9.59%(95%CI:7.04%—12.21%)。

秸秆还田在黏土、壤土及砂土3种土壤质地中对农作物的增产效应没有显著差异(图2-d),增产率分别为(8.13±1.36)%、(9.04±0.97)%、(6.96±1.81)%;图2-e表明,秸秆还田在弱酸性土壤中更能发挥其增产效应,增产率达到12.46%(95%CI:10.92%—14.01%),中性土壤中的增产效应次之,增产率为7.73%(95%CI:6.90%—8.57%),碱性土壤中增产效应最低,增产率为6.35%(95%CI:4.74%—7.98%)。

在主要的粮食作物中,秸秆还田的产量效应表现出显著的差异(图2-f)。对玉米的增产作用最大,增产率达到9.22%(95%CI:8.38%—10.05%);对水稻的增产作用次之(增产率(7.58±1.03)%),对小麦的增产作用最低(增产率(5.75±0.86)%),均低于总体的平均水平。图2-g表明秸秆还田在一年一熟制中的增产作用显著高于一年两熟制,增产率分别为8.99%(95%CI:8.00%—9.99%)、7.65%(95%CI:7.00%—8.30%)。

免耕与翻耕是最有利于发挥秸秆还田增产作用的耕作方式。翻耕秸秆还田的增产率为11.05%,95%CI为10.05%—12.05%,免耕秸秆还田的增产率为8.98%,95%CI为7.21%—10.79%,均高于总体的平均增产率。旋耕与深耕秸秆还田的增产率分别为(6.57±1.27)%、(6.36±1.96)%,均低于总体的平均增产率(图2-h)。

秸秆还田在正常的施肥模式下能够提高8.08%(95%CI:7.50%—8.68%)的作物产量(图2-i),与总体的平均增产率一致,其他施肥方式都会影响秸秆还田的增产效应。不施任何肥料时,秸秆还田对农作物的增产效应显著提高,增产率达到25.66%(95%CI:22.04%—29.38%),但不施肥时整体的产量水平较低;不施钾肥、正常施用氮磷肥时,秸秆还田的增产率最低,为4.08%(95%CI:2.33%—5.87%);不施氮肥、正常施用磷钾肥时,秸秆还田的增产率为7.24%(95%CI:4.12%—10.46%);低施氮量与高施氮量下,秸秆还田的增产率分别为6.40%(95%CI:4.09%—8.76%)、6.08%(95%CI:2.63%—9.64%);可见,不同氮肥施用方式对秸秆还田的产量效应并无显著影响。

随着秸秆还田持续年限的增加,秸秆还田的增产率在短期内略微降低后持续增加(图2-j)。持续秸秆还田超过10年、15年、20年时,相应的增产率分别达到9.74%(95%CI:7.59%—11.92%)、13.88%(95%CI:9.78%—18.13%)、15.42%(95%CI:11.05%—19.95%),增产率的提高趋势显著。

不同的秸秆还田量对还田后农作物产量的影响不显著(图2-k)。利用前茬作物50%—100%的秸秆进行还田时增产作用最高,增产率为9.09%(95%CI:7.41%—10.79%),高于秸秆全量还田后(7.91±0.59)%的增产率。秸秆还田量低于50%和超过100%的相关研究较少,仅有的研究结果表明两者的增产率分别为8.72%(95%CI:5.13%—12.42%)、8.21%(95%CI:3.84%—12.77%)。

3 讨论

3.1 秸秆还田的增产效应

本研究结果表明,与秸秆不还田相比,秸秆还田显著提高了农作物产量,增产率达到8.06%(95%CI:7.52%—8.60%,不存在发表偏倚)。与秸秆不还田相比,秸秆还田显著改善了土壤物理性状,降低了土壤容重[42],增加了土壤孔隙度[43],促进了土壤微团聚体向大团聚体的转化,土壤固碳能力显著提升[44];秸秆还田后土壤中的养分含量显著增加,0—60 cm土层中的土壤有机碳及全氮含量显著提高[45],减少了磷流失,提高了土壤磷储量[46],同时提高了土壤中的全钾及速效钾含量[47];长期秸秆还田改变了土壤中的微生物群落结构,显著提高了土壤中的真菌、放线菌丰富度,且随着秸秆投入量的增加,几种土壤水解酶的活性也随之提高[48]。秸秆还田通过改善土壤的理化性质,提高了土壤生产力,实现增产。

3.2 影响秸秆还田增产效应的因素

3.2.1 试验区域 由于不同生态区域农业生产条件的差异性,农业生产呈现出明显的地域分异[49]。本研究表明,秸秆还田对总体农作物的增产率在不同试验区域内存在差异,但增产效应差异不显著。宋大利等[50]对我国农作物秸秆产量及养分含量地区分布进行了研究,结果表明西北地区的农作物秸秆资源无论是在产量方面还是在养分含量方面,在所有地区中都是最低的,东南地区(以长江中下游地区为主)的农作物秸秆产量虽然没有达到最高的产量,但秸秆养分含量却是所有地区中最高的。基于此,可以解释本研究中秸秆还田对总体农作物的增产效应在东南地区最高而在西北地区最低。

3.2.2 土壤质地及酸碱性 土壤质地是土壤中各级别土粒的质量百分比,分为砂土、壤土及黏土三大类,是土壤重要的物理特性,在很大程度上支配土壤的耕作性能[22],不同作物在不同土壤质地中表现出不同的氮代谢特征[51],但不同土壤质地中秸秆还田增产效应的差异机理有待进一步研究。酸性土壤中秸秆还田的增产效应更高,可能是由于农作物秸秆为碱性物质,还田后降低了土壤酸性,土壤保肥能力增强[52];而在碱性土壤中,即使耐盐性较强的作物(棉花),其产量也会降低[53]。因此,通过秸秆还田可以获得最适于农业生产的土壤酸碱性,但最适的土壤pH范围有待进一步研究。

3.2.3 种植制度 土壤长期耕作会导致土壤钾素消

耗,与单作相比,轮作消耗更严重,而秸秆还田可以增加土壤速效钾的含量,另外配合使用钾肥,能够缓解土壤钾素的耗竭,从而增加小麦、玉米的产量[54]。一年二熟区秸秆资源丰富,秸秆还田能增加土壤有机质,补充土壤养分,维持土壤的可持续发展,对于增产增收有一定促进作用[55,56]。不同作物熟制中秸秆还田的增产效应没有显著差异,在一年一熟制中,秸秆还田避免了作物生长发育早期受到低温胁迫的影响[57],在一年两熟制地区具有充足的光热资源及丰富的降水量,秸秆还田也有利于农作物生长[58]

3.2.4 耕作方式 耕作是在播种前,对土壤、杂草和农作物秸秆进行的综合处理[59]。不同的耕作方式显著影响农作物秸秆在土壤中的空间分布状况[60]。适宜的耕作方式会加快秸秆的分解速率,改善土壤微生物活性,有利于发挥秸秆还田的增产作用[61]。本研究主要涉及免耕、翻耕、深耕及旋耕4种耕作方式,分析结果表明免耕或翻耕条件下秸秆还田的增产效应显著高于深耕或旋耕方式。免耕是指作物播前不用犁、耙整理土地,不清理作物残茬,直接在原茬地上播种,播后作物生育期间不使用农具进行土壤管理[62],是保护性耕作的一种,多应用于我国西北干旱地区,保温保墒;免耕显著提高了表层土壤的有机质含量,具有防止水土流失、改善土壤物理性质、增产降水储存、保持土壤水分等优势[63,64],秸秆还田降低了免耕对农作物的减产程度[65],在短期内对农作物具有增产作用[66]。翻耕的耕层一般在20—30 cm[67],翻耕秸秆还田获得的增产率最高。将农作物秸秆翻埋至20 cm土层中有助于农作物秸秆的分解,土壤中的有机质、全氮、速效氮、速效磷及速效钾含量均有所增加,且亚耕层(20—40 cm)土壤养分含量的增幅更加显著;秸秆深翻还田有效改善了对农业生产中存在的耕层浅、养分少等[68]土壤肥力退化问题,显著提高了农作物产量[69]。深耕一般耕层在30—60 cm[70],是在不翻动上层贫瘠土壤的前提下对硬质土壤和压实土壤的破坏过程[71],深耕秸秆还田降低了土壤容重及土壤渗透阻力,能够缓解了土壤中的水分耗竭,提高农作物的水分利用效率,但增产效应未达到显著水平[72]。旋耕的耕层一般在15—20 cm[67],旋耕作业操作简便、作业成本低,但旋耕秸秆还田使得农作物秸秆大量聚集在土壤表面,形成了缺氧环境,在秸秆腐解时可能会产生H2S等对农作物生长有害的物质,降低了秸秆还田的增产效应[73];同时,土壤耕层变浅、容重增加、孔隙度降低,农作物秸秆的物理阻碍导致作物出苗率低、成穗率低,引起产量下滑[74]。庞党伟等[75]在长期旋耕秸秆还田的基础上进行深耕,平衡了各土层的有效养分含量,显著改善了10—30 cm土层的微生物活性及下层土壤物理性状,促进农作物根系下扎对养分的吸收,实现了增产。

3.2.5 施肥模式 农作物秸秆具有巨大的肥料替代潜力,在全部的化肥投入中,全量秸秆还田能够替代全部的钾肥投入、28.77%的磷肥投入及24.25%的氮肥投入;在有效利用的化肥中,全量秸秆还田能够替代全部的有效钾肥、有效磷肥及90%的有效氮肥,也就是说,秸秆移除在一定程度上造成了土壤养分损失[76]。本研究结果表明,不施肥或不施氮肥时能够获得较高秸秆还田增产效应,尤其是不施肥时的增产效应显著高于其他施肥方式,但整体的产量水平显著降低[77],这与前人的整合(Meta)分析结果一致[78]。就不同的氮肥水平而言,对于养分含量充足的肥沃土壤来说,无论农作物秸秆是否还田,适度减少氮肥施用并未对土壤的化学性质及微生物群落结构产生显著影响,不会改变土壤原有的生态系统功能[79];同时,适度减少氮肥投入在维持高产的同时显著降低了N2O等温室气体的排放量[80]。也有研究表明,随着施氮量的增加,秸秆还田的增产效应也随之增加,而氮肥偏生产力降低,这可能与秸秆还田的深度有关[81]。考虑到高施氮量时生产成本的提高以及潜在的环境污染风险,秸秆还田配施低量氮肥应该是更为合理的农业生产方式[82]。此外,秸秆还田时需要施入钾肥,以缓解土壤中钾的耗竭、提高土壤的钾肥力,维持作物高产[83]

3.2.6 还田年限 本研究表明,秸秆还田对总体农作物的增产效应在年际间的增长趋势显著,可能是由于耕地资源的开发、农业生产技术的改良以及育种科学发展提供了优质的种质资源[84],产量潜力更高的农作物种子配套先进的农业生产技术能够获得更高的产量。随着还田年限的增加,秸秆还田的增产效应也随之显著提高,这与前人的整合(Meta)分析结果一致[78]。相关研究表明,秸秆还田对土壤有机质的影响是一个长期的过程,短时间内很难发生显著的变化[85],长期秸秆还田后,各土层的土壤养分明显改善,尽管土壤中的有机质积累量减少,但总含量始终稳定在较高水平[86]。因此,长期秸秆还田的增产效应会更加显著。此外,LI等[87]的研究还发现长期秸秆还田还能够获得更高的经济效益,是维持作物高产最有效的方式。

3.2.7 秸秆还田量 本研究结果表明,不同的秸秆还田量对秸秆还田的增产效应没有显著影响,以50%—100%的农作物秸秆产量进行还田获得的增产水平相对较高。与全量秸秆还田相比,半量秸秆还田显著提高了耕层土壤孔隙度、降低了土壤容重,有利于根系在深层土壤中的生长延伸,改善了深层土壤根系密度及根系结构,产量水平略高于全量秸秆还田[88],同时显著降低了田间温室气体的排放量,保证了土壤的可持续性[89]。也有研究表明,全量秸秆还田显著提高了土壤养分及土壤酶活性,同时提高了农作物产量及水分利用效率,增产效应显著高于半量秸秆还田,但更高的秸秆还田量没有进一步提高产量水平[90]。此外,小麦秸秆半量、全量还田均显著降低了水稻中Cd向上的转运能力,降低了水稻地上部分Cd累积量(P<0.05)。总体而言,全量秸秆还田在全生育期土壤中DGT提取态Cd含量显著低于半量还田,解毒效果更好[91,92]。研究结果发现,不同的秸秆还田量在促进早、晚稻分蘖的效果上具有差异,在早稻季低量还田优于高量,而在晚稻季则相反。究其原因可能是早、晚稻生育期间的温度差异而致[93]

3.3 本研究的不足之处

本研究通过整合(Meta)分析方法对秸秆还田下农作物的产量特征进行了定量分析,分析结果在一定程度上证明了秸秆还田具有显著的增产效应,与绝大多数的田间试验结果一致,仅有极少数的田间试验研究发现秸秆还田不存在显著的增产效应、甚至会降低农作物产量。同时,本研究也存在一定的局限性。秸秆还田的增产效应受各种因素的影响,包括但不仅限于本研究中所讨论的,可能的影响因素还有很多,比如农业生产中使用的不同小麦品种、玉米品种、还田秸秆的长度、土壤的基础肥力等,文献中涉及的相关数据较少不易提取,需要更多的田间试验结果,因此本研究中并未讨论;本研究充分考虑了全国范围内秸秆还田的产量效应,对于宏观上认识秸秆还田的增产作用具有一定意义,但与全国范围内不同区域的实际情况或特定作物种类缺乏联系,进一步的分析研究需要与实际生产问题结合,考察不同区域内秸秆还田的增产效应。此外,本研究只收集整理了中文文献,缺少高质量的英文文献。

4 结论

全国范围内,农作物秸秆还田具有显著的增产效应,增产率达到8.06%。不同试验地区秸秆还田的增产效应没有显著差异,东南地区较高,西北地区较低。在年平均气温为5—10℃、年平均降水量达到800— 1 200 mm的酸性壤土(pH<6.5)中,秸秆还田对农作物的增产率相对较高;在气候干旱地区采用免耕、在农作物秸秆产量丰富地区采用翻耕更能发挥秸秆还田的增产效应;在牺牲部分产量的情况下可以适当减少氮肥的施用量,能够兼顾经济与环境效益;合理的还田量为半量还田,不还田的剩余秸秆可以作饲料或生产其他能源物质;长期秸秆还田能够获得更高的增产效应。

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Acta Agriculturae Boreali-Sinica, 2019,34(6):177-183. (in Chinese)

[本文引用: 1]

ZHAO S C, LI K J, ZHOU W, QIU S J, HUANG S W, HE P. Changes in soil microbial community, enzyme activities and organic matter fractions under long-term straw return in north-central China
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[本文引用: 1]

刘彦随, 张紫雯, 王介勇. 中国农业地域分异与现代农业区划方案
地理学报, 2018,73(2):203-218.

[本文引用: 1]

LIU Y S, ZHANG Z W, WANG J Y. Regional differentiation and comprehensive regionalization scheme of modern agriculture in China
Acta Geographica Sinica, 2018,73(2):203-218. (in Chinese)

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宋大利, 侯胜鹏, 王秀斌, 梁国庆, 周卫. 中国秸秆养分资源数量及替代化肥潜力
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SONG D L, HOU S P, WANG X B, LIANG G Q, ZHOU W. Nutrient resource quantity of crop straw and its potential of substituting
Journal of Plant Nutrition and Fertilizers, 2018,24(1):1-21. (in Chinese)

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熊淑萍, 张娟娟, 杨阳, 刘娟, 王晓航, 吴延鹏, 马新明. 不同冬小麦品种在3种质地土壤中氮代谢特征及利用效率分析
植物生态学报, 2013,37(7):601-610.

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XIONG S P, ZHANG J J, YANG Y, LIU J, WANG X H, WU Y P, MA X M. Research on nitrogen metabolism characteristics and use efficiency in different winter wheat cultivars grown on three soil textures
Chinese Journal of Plant Ecology, 2013,37(7):601-610. (in Chinese)

[本文引用: 1]

刘成, 刘晓雨, 张旭辉, 李恋卿, 潘根兴. 基于整合分析方法评价我国生物质炭施用的增产与固碳减排效果
农业环境科学学报, 2019,38(3):696-706.

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LIU C, LIU X Y, ZHANG X H, LI L Q, PAN G X. Evaluating the effects of biochar amendment on crop yield and soil carbon sequestration and greenhouse gas emission using meta-analysis
Journal of Agro- Environment Science, 2019,38(3):696-706. (in Chinese)

[本文引用: 1]

秦都林, 王双磊, 刘艳慧, 聂军军, 赵娜, 毛丽丽, 宋宪亮, 孙学振. 滨海盐碱地棉花秸秆还田对土壤理化性质及棉花产量的影响
作物学报, 2017,43(7):1030-1042.

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QIN D L, WANG S L, LIU Y H, NIE J J, ZHAO N, MAO L L, SONG X L, SUN X Z. Effects of cotton stalk returning on soil physical and chemical properties and cotton yield in coastal saline-alkali soil
Acta Agronomica Sinica, 2017,43(7):1030-1042. (in Chinese)

[本文引用: 1]

谭德水, 金继运, 黄绍文, 李书田, 何萍. 不同种植制度下长期施钾与秸秆还田对作物产量和土壤钾素的影响
中国农业科学, 2007,40(1):133-139.

[本文引用: 1]

TAN D S, JIN J Y, HUANG S W, LI S T, HE P. Effect of long-term application of K fertilizer and wheat straw to soil on crop yield and soil K under different planting systems
Scientia Agricultura Sinica, 2007,40(1):133-139. (in Chinese)

[本文引用: 1]

姜井军, 石广跃. 麦玉轮作和麦稻轮作制度下的秸秆还田技术研究进展
农业科技通讯, 2014(11):135-138.

[本文引用: 1]

JIANG J J, SHI G Y. Research progress on straw mulching technology under wheat-and-rice and wheat-and-jade rotation system
Bulletin of Agricultural Science and Technology, 2014(11):135-138. (in Chinese)

[本文引用: 1]

王宏庭, 金继运, 王斌, 赵萍萍. 山西褐土长期施钾和秸秆还田对冬小麦产量和钾素平衡的影响
植物营养与肥料学报, 2010,16(4):801-808.

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WANG H T, JIN J Y, WANG B, ZHAO P P. Effects of long-term potassium application and wheat straw return to cinnamon soil on wheat yields and soil potassium balance in Shanxi
Journal of Plant Nutrition and Fertilizers, 2010,16(4):801-808. (in Chinese)

[本文引用: 1]

QI G.P, KANG Y X, YIN M H, MA Y L, BAI Y S, WANG J H. Yield responses of wheat to crop residue returning in China: A meta-analysis
Crop Science, 2019,59(5):2185-2200.

[本文引用: 1]

肖琼, 王齐齐, 邬磊, 蔡岸冬, 王传杰, 张文菊, 徐明岗. 施肥对中国农田土壤微生物群落结构与酶活性影响的整合分析
植物营养与肥料学报, 2018,24(6):1598-1609.

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XIAO Q, WANG Q Q, WU L, CAI A D, WANG C J, ZHANG W J, XU M G. Fertilization impacts on soil microbial communities and enzyme activities across China’s croplands: A meta-analysis
Journal of Plant Nutrition and Fertilizers, 2018,24(6):1598-1609. (in Chinese)

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OBOUR P B, JENSEN J L, LAMANDE M, WATTS C W, MUNKHOLM L J. Soil organic matter widens the range of water contents for tillage
Soil & Tillage Research, 2018,182(1):57-65.

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ZHOU H, ZHANG C L, ZHANG W L, YANG Q J, LI D, LIU Z Y, XIA J F. Evaluation of straw spatial distribution after straw incorporation into soil for different tillage tools
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SUN B J, JIA S X, ZHANG S X, MCLAUGHLIN N B, ZHANG X P, LIANG A Z, CHEN X W, WEI S C, LIU S Y. Tillage, seasonal and depths effects on soil microbial properties in black soil of Northeast China
Soil & Tillage Research, 2016,155:421-428.

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高云超, 朱文珊. 秸秆覆盖免耕土壤微生物生物量与养分转化的研究
中国农业科学, 1994,27(6):41-49.

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GAO Y C, ZHU W S. The relationship between soil microbial biomass and the transformation of plant nutrients in straw mulched no-tillage soils
Scientia Agricultura Sinica, 1994,27(6):41-49. (in Chinese)

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DONG W Y, LIU E K, WANG J B, YAN C G, LI J, ZHANG Y Q. Impact of tillage management on the short- and long-term soil carbon dioxide emissions in the dryland of Loess Plateau in China
Geoderma, 2017,307:38-45.

[本文引用: 1]

ABDULLAH A S. Minimum tillage and residue management increase soil water content, soil organic matter and canola seed yield and seed oil content in the semiarid areas of Northern Iraq
Soil & Tillage Research, 2014,144(1):150-155.

[本文引用: 1]

PITTELKOW C M, LIANG X, LINQUIST B A, JAN VAN GROENIGEN K, LEE J, LUNDY M E, VAN GESTEL N, SIX J, VENTEREA R T, VAN KESSEL C. Productivity limits and potentials of the principles of conservation agriculture
Nature, 2014,517(7534):365-368.

DOI:10.1038/nature13809URLPMID:25337882 [本文引用: 1]
One of the primary challenges of our time is to feed a growing and more demanding world population with reduced external inputs and minimal environmental impacts, all under more variable and extreme climate conditions in the future. Conservation agriculture represents a set of three crop management principles that has received strong international support to help address this challenge, with recent conservation agriculture efforts focusing on smallholder farming systems in sub-Saharan Africa and South Asia. However, conservation agriculture is highly debated, with respect to both its effects on crop yields and its applicability in different farming contexts. Here we conduct a global meta-analysis using 5,463 paired yield observations from 610 studies to compare no-till, the original and central concept of conservation agriculture, with conventional tillage practices across 48 crops and 63 countries. Overall, our results show that no-till reduces yields, yet this response is variable and under certain conditions no-till can produce equivalent or greater yields than conventional tillage. Importantly, when no-till is combined with the other two conservation agriculture principles of residue retention and crop rotation, its negative impacts are minimized. Moreover, no-till in combination with the other two principles significantly increases rainfed crop productivity in dry climates, suggesting that it may become an important climate-change adaptation strategy for ever-drier regions of the world. However, any expansion of conservation agriculture should be done with caution in these areas, as implementation of the other two principles is often challenging in resource-poor and vulnerable smallholder farming systems, thereby increasing the likelihood of yield losses rather than gains. Although farming systems are multifunctional, and environmental and socio-economic factors need to be considered, our analysis indicates that the potential contribution of no-till to the sustainable intensification of agriculture is more limited than often assumed.

POWLSON D S, STIRLING C M, JAT M L, GERAAD B G, PALM C A, SANCHEZ P A, CASSMAN K G. Limited potential of no-till agriculture for climate change mitigation
Nature Climate Change, 2014,4(8):678-683.

[本文引用: 1]

王群, 王建, 张学林, 赵亚丽, 李潮海. 不同耕作模式下小麦玉米周年生产及土壤养分变化特征
河南农业大学学报, 2015,49(4):429-437.

[本文引用: 2]

WANG Q, WANG J, ZHANG X L, ZHAO Y L, LI C H. Change characteristics of wheat and maize anniversary production and soil nutrient content under different roration tillage patterns
Journal of Henan Agricultural University, 2015,49(4):429-437. (in Chinese)

[本文引用: 2]

吴萍萍, 李录久, 耿言安, 姚文麒. 耕作与施肥措施对江淮地区白土理化性质及水稻产量的影响
水土保持学报, 2018,32(6):243-248.

[本文引用: 1]

WU P P, LI L J, GENG Y A, YAO W Q. Effects of tillage and fertilization on physicochemical properties of albic soil and rice yields in Jianghuai region
Journal of Soil and Water Conservation, 2018,32(6):243-248. (in Chinese)

[本文引用: 1]

蔡红光, 梁尧, 刘慧涛, 刘剑钊, 秦裕波, 刘方明, 袁静超, 张洪喜, 任军, 王立春. 东北地区玉米秸秆全量深翻还田耕种技术研究
玉米科学, 2019,27(5):123-129.

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CAI H G, LIANG Y, LIU H T, LIU J Z, QIN Y B, LIU F M, YUAN J C, ZHANG H X, REN J, WANG L C. Research on full maize straw returning with deep ploughing mode in the northeast China
Journal of Maize Sciences, 2019,27(5):123-129. (in Chinese)

[本文引用: 1]

胡振琪, CHONG S K. 深耕对复垦土壤物理特性改良的研究
土壤通报, 1999(6):248-250, 264.

[本文引用: 1]

HU Z Q, CHONG S K. Study on the improvement of physical properties of reclaimed soil by deep tillage
Chinese Journal of Soil Science, 1999(6):248-250, 264. (in Chinese)

[本文引用: 1]

MA S Y, YU Z W, SHI Y, GAO Z Q, LUO L P, CHU P F, GUO Z J. Soil water use, grain yield and water use efficiency of winter wheat in a long-term study of tillage practices and supplemental irrigation on the North China Plain
Agricultural Water Management, 2015,150:9-17.

[本文引用: 1]

XIE J H, WANG L L, LI L L, COULTER J A, CHAI Q, ZHANG R Z, LUO Z Z, CARBERRY P, RAO K P C. Subsoiling increases grain yield, water use efficiency, and economic return of maize under a fully mulched ridge-furrow system in a semiarid environment in China
Soil & Tillage Research, 2020,199. doi: 10.1016/j.still.2020.104584.

DOI:10.1016/j.still.2020.104595URLPMID:32362695 [本文引用: 1]
Climate smart agriculture (CSA) practices are emerging as sustainable alternative to conventional rice-wheat system to pull up natural resources degradation across south Asia. After five years of continuous CSA based experiment, a two years study was conducted to evaluate changes in microbial biomasses (microbial biomass carbon and nitrogen), enzyme activities (alkaline phosphatase, dehydrogenase and beta-glucosidase), nutrient release and uptake (N, P and K) at different wheat crop growth stages. Effect of CSA practices was also studied for carbon mineralization in an incubation experiment. Four scenarios (Sc) were included in this study- conventional tillage (CT) based rice-wheat system (Sc1), partial CSA based rice-wheat-mungbean system (Sc2), full CSA based rice-wheat-mungbean system (Sc3), and full CSA based maize-wheat-mungbean system (Sc4). Soil samples were collected from scenarios at 0-15 and 15-30 cm depth at different growth stages of wheat crop namely sowing, crown root initiation (CRI), active tillering, panicle initiation, and harvesting. Analysis of soil was done for chemical properties viz. pH, electrical conductivity, available N, P, K, NPK uptake and mineralizable carbon and biological properties viz., microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), dehydrogenase activity (DHA), alkaline phosphatase activity (APA) and beta-glucosidase. Significantly higher microbial biomass carbon (42 %) and nitrogen (79 %) were found in surface soil (0-15 cm depth) under CSA based scenarios (Sc2, Sc3 and Sc4) at harvest stage of wheat over CT based/ conventional scenario (Sc1). At surface soil, alkaline phosphatase, dehydrogenase and beta-glucosidase activity was 58, 14 and 13 % higher in CSA based scenarios, respectively than CT based scenario. CSA based scenarios showed significantly higher C mineralization after 3 days of the incubation experiment at harvest. An increase of respectively 15, 48 and 17 % of N, P and K uptake was observed with CSA based scenarios than CT based scenario. At harvest stage, 7 % higher amount of dry matter was reported with full CSA based scenarios (mean of Sc2 to Sc4) compared to Sc1. Higher wheat grain yield of approximately 10 % was recorded with CSA based scenarios over CT based scenario. Therefore, CSA based scenarios with improved biological properties and nutrient availability and uptake at different wheat growth stages resulted in higher yields and hence need to be popularized among the farmers.

ZHANG J, HAN X N, LAMINE S M, JIANG Y, AFREH D, QIAN H Y, FENG X M, ZHENG C Y, DENG Z W, ZHANG W J. Interactive effects of straw incorporation and tillage on crop yield and greenhouse gas emissions in double rice cropping system
Agriculture, Ecosystems & Environment, 2017,250:37-43.

[本文引用: 1]

陈金, 庞党伟, 韩明明, 尹燕枰, 郑孟静, 骆永丽, 王振林, 李勇. 耕作模式对土壤生物活性与养分有效性及冬小麦产量的影响
作物学报, 2017,43(8):1245-1253.

[本文引用: 1]

CHEN J, PANG D W, HAN M M, YIN Y P, ZHENG M J, LUO Y L, WANG Z L, LI Y. Effects of tillage patterns on soil biological activity, availability of soil nutrients and grain yield of winter wheat
Acta Agronomica Sinica, 2017,43(8):1245-1253. (in Chinese)

[本文引用: 1]

庞党伟, 陈金, 唐玉海, 尹燕枰, 杨东清, 崔正勇, 郑孟静, 李勇, 王振林. 玉米秸秆还田方式和氮肥处理对土壤理化性质及冬小麦产量的影响
作物学报, 2016,42(11):1689-1699.

[本文引用: 1]

PANG D W, CHEN J, TANG Y H, YIN Y P, YANG D Q, CUI Z Y, ZHENG M J, LI Y, WANG Z L. Effect of returning methods of maize straw and nitrogen treatments on soil physicochemical property and yield of winter wheat
Acta Agronomica Sinica, 2016,42(11):1689-1699. (in Chinese)

[本文引用: 1]

YIN H J, ZHAO W Q, LI T, CHENG X Y, LIU Q. Balancing straw returning and chemical fertilizers in China: Role of straw nutrient resources
Renewable and Sustainable Energy Reviews, 2018,81(2):2695-2702.

[本文引用: 1]

AKHTAR K, WANG W Y, REN G X, KHAN A, FENG Y Z, YANG G H, WANG H Y. Integrated use of straw mulch with nitrogen fertilizer improves soil functionality and soybean production
Environment International, 2019,132. doi: 10.1016/j.envint.2019. 105092.

DOI:10.1016/j.envint.2019.105068URLPMID:31470219 [本文引用: 1]
BACKGROUND: Chemical UV filters are common components in sunscreens and cosmetic products and used to protect the skin against harmful effects of sunlight like sunburn. However, the effectiveness of sunscreens in the prevention of skin cancer is in some parts still controversial. Meanwhile, questions about negative effects of the chemical UV filters on human health arise and request an effective risk assessment. Real-life exposure data in humans after application of these products are still rare. Thus, we explored whether and to what extent UV filters are absorbed through the skin into the human body. MATERIAL AND METHODS: Plasma and urine samples from 20 healthy volunteers were collected before, during and after a real-life exposure scenario (1st application: 2mg/cm(2); 2nd and 3rd (after 2 and 4h): 1mg/cm(2) each) using a commercial sunscreen formulation for one day. These samples were analyzed for their content of the currently prominent UV filters octocrylene and avobenzone as well as 2-cyano-3,3-diphenylacrylic acid (CDAA) as the main octocrylene metabolite by using different liquid chromatography electrospray-ionization tandem mass spectrometric procedures. RESULTS: Following dermal sunscreen exposure, avobenzone, octocrylene and CDAA reached concentrations up to 11mug/L, 25mug/L and 1352mug/L in plasma. In urine detection rates of avobenzone and octocrylene were low while CDAA showed a high detection rate and reached up to 5207mug/g creatinine. Kinetic models could be fitted for octocrylene and CDAA in plasma and CDAA in urine. Concentration peaks were reached between 10 and 16h after first application and half-life periods were in the range of 1.5 to 2days. The lipophilic UV filter octocrylene and its metabolite CDAA showed a much slower elimination than other more hydrophilic UV filters. Concordantly, the metabolite CDAA in particular showed a markedly increased renal excretion over the whole sampling period and indicated high internal exposure to OC. DISCUSSION: Real-life sunscreen usage leads to considerable bioavailability of organic UV filters and their metabolites which is rarely seen for other environmental exposures. A combined monitoring of the parent compound and its metabolites is important to fully address internal exposure to the UV filter in humans. Considering the kinetic profiles a prolonged systemic release due to depot formation in skin and a potential accumulation through multi-day exposure is presumed. High in-vivo loads call for a critical toxicological assessment of the UV filters and their metabolites.

HUANG S, ZENG Y J, WU J F, SHI Q H, PAN X H. Effect of crop residue retention on rice yield in China: A meta-analysis
Field Crops Research, 2013,154:188-194.

[本文引用: 2]

LI H, ZHANG Y Y, YANG S, WANG Z R, FENG X, LIU H Y, JIANG Y. Variations in soil bacterial taxonomic profiles and putative functions in response to straw incorporation combined with N fertilization during the maize growing season
Agriculture, Ecosystems & Environment, 2019,283:106578.

[本文引用: 1]

TAN Y C, WU D, BOL R, WU W L, MENG F Q. Conservation farming practices in winter wheat-summer maize cropping reduce GHG emissions and maintain high yields
Agriculture, Ecosystems & Environment, 2019,272:266-275.

[本文引用: 1]

周珂, 王晓军, 李华芝, 徐欣, 高洪生, 焦晓光. 秸秆深埋条件下不同施氮水平对玉米产量和氮吸收利用的影响
中国农学通报, 2019,35(33):6-11.

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ZHOU K, WANG X J, LI H Z, XU X, GAO H S, JIAO X G. The effects of different nitrogen application levels on maize yield and the absorption and utilization of nitrogen under straw deep burial
Chinese Agricultural Science Bulletin, 2019,35(33):6-11. (in Chinese)

[本文引用: 1]

DOSSOU-YOVO E R, BRUGGEMANN N, AMPOFO E, IGUE A M, JESSE N, HUAT J, AGBOSSOU E K. Combining no-tillage, rice straw mulch and nitrogen fertilizer application to increase the soil carbon balance of upland rice field in northern Benin
Soil & Tillage Research, 2016,163:152-159.

[本文引用: 1]

ZHAO S C, HE P, QIU S J, JIA L L, LIU M C, JIN J Y, JOGNSTON A M. Long-term effects of potassium fertilization and straw return on soil potassium levels and crop yields in north-central China
Field Crops Research, 2014,169:116-122.

DOI:10.1016/j.fcr.2014.09.017URL [本文引用: 1]

盖钧镒, 刘康, 赵晋铭. 中国作物种业科学技术发展的评述
中国农业科学, 2015,48(17):3303-3315.

[本文引用: 1]

GAI J Y, LIU K, ZHAO J M. A Review on Advances in Science and Technology in Chinese Seed Industry
Scientia Agricultura Sinica, 2015,48(17):3303-3315. (in Chinese)

[本文引用: 1]

ZHENG L, WU W L, WEI Y P, HU K L. Effects of straw return and regional factors on spatio-temporal variability of soil organic matter in a high-yielding area of northern China
Soil & Tillage Research, 2015,145:78-86.

[本文引用: 1]

ZHANG Z Q, QIANG H J, MCHUGH A D, HE J, LI H W, WANG Q J, LU Z Y. Effect of conservation farming practices on soil organic matter and stratification in a mono-cropping system of Northern China
Soil & Tillage Research, 2016,156:173-181.

[本文引用: 1]

LI Z, YANG X, CUI S, YANG Q, YANG X L, LI J C, SHEN Y Y. Developing sustainable cropping systems by integrating crop rotation with conservation tillage practices on the Loess Plateau, a long-term imperative
Field Crops Research, 2018,222(4):164-179.

DOI:10.1016/j.fcr.2018.03.027URL [本文引用: 1]

XU X, PANG D W, CHEN J, LUO Y L, ZHENG M J, YIN Y P, LI Y X, LI Y, WANG Z L. Straw return accompany with low nitrogen moderately promoted deep root
Field Crops Research, 2018,221:71-80.

DOI:10.1016/j.fcr.2018.02.009URL [本文引用: 1]

GAO F, LI B, REN B Z, ZHAO B, LIU P, ZHANG J W. Effects of residue management strategies on greenhouse gases and yield under double cropping of winter wheat and summer maize
Science of the Total Environment, 2019,687:1138-1146.

DOI:10.1016/j.scitotenv.2019.06.146URL [本文引用: 1]

WANG X J, JIA Z K, LIANG L Y, ZHAO Y F, YANG B P, DING R X, WANG J P, NIE J F. Changes in soil characteristics and maize yield under straw returning system in dryland farming
Field Crops Research, 2018,218:11-17.

DOI:10.1016/j.fcr.2017.12.003URL [本文引用: 1]

黄界颍, 武修远, 佟影影, 曹森, 高越, 杨卉艳. 小麦秸秆还田量对土壤Cd有效性及水稻Cd亚细胞分布的影响
农业环境科学学报, 2020,39(7):1503-1511.

[本文引用: 1]

HUANG J Y, WU X Y, TONG Y Y, CAO S, GAO Y, YANG H Y. Effects of returning wheat straw on available cadmium and subcellular distribution of cadmium in rice
Journal of Agro- Environment Science, 2020,39(7):1503-1511. (in Chinese)

[本文引用: 1]

段桂兰, 王芳, 岑况, 王伯勋, 程旺大, 刘跃川, 张红梅. 秸秆还田对水稻镉积累及其亚细胞分布的影响
环境科学, 2017,38(9):3927-3936.

[本文引用: 1]

DUAN G L, WANG F, CEN K, WANG B X, CHENG W D, LIU Y C, ZHANG H M. Effects of straw incorporation on cadmium accumulation and subcellular distribution in rice
Environmental Science, 2017,38(9):3927-3936. (in Chinese)

[本文引用: 1]

胡明芳, 赵振勇, 张科. 周年秸秆还田量对南方双季稻生长及产量的影响
中国农学通报, 2020,36(4):1-6.

[本文引用: 1]

HU M F, ZHAO Z Y, ZHANG K. Effect of annual straw returning amount on growth and yield of double cropping rice in south China
Chinese Agricultural Science Bulletin, 2020,36(4):1-6. (in Chinese)

[本文引用: 1]

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