Conservation Tillage Effects on European Crop Yields: A Metadata Analysis

We were hypothesizing that ridge-till and strip-till can improve crop yields under European agroecosystems in contrast with the often observed reduction in yields under No Tillage and Reduced Tillage.To answer the hypothesized question, and based on our data, it can be stated that yields are indeed not optimal under no till as opposed to RT and ST where yields are higher than those of conventional tillage.
The raw data shows every parameter and data extracted from peer reviewed articles while the analyzed shows the data obtained from running the meta-analysis by fitting random and mixed effect models.

Raw data
Here, the data consists of all parameters and variables extracted from the peer-reviewed articles that met the inclusion criteria. Also, there is a metadata sheet with all abbreviations and their meaning.

Analyze data
Here, the estimate, lower (ci.lb) and upper (ci.ub) confidence intervals are in log transform format. Thus, taking the exponent gives the response ratio. Se =standard error, zval= z correlation, pval =p value, RT (ridge-till) and RH (number) = ridge height. C =Cotton, Cr = Carrot, RD = Dandellion, T=Tulips, GM= grain maize, WC= winter cereal, SB =sugar beet, SC=spring cereal, P =potato. Also, there is a metadata sheet with all abbreviations and their meaning.

Graph
Fig. 1. Forest plot showing data plot of estimated average response ratios (RR) for No (NT), Ridge (RT) and Strip (ST) tillage based on random effects model. n is sample size/number of studies for the specific tillage type. The error bars represent the 95% confidence intervals CI. The square shapes represent the estimated RR and their size correspond to their weight (based on its sample size). The vertical broken line represents the line of no effect

我们提出了假设，认为在欧洲农业生态系统下，与常观察到的免耕和少耕条件下产量降低的情况相比，脊耕和条耕可以提升作物产量。为了解答这一假设性问题，并基于我们的数据，可以明确指出，与脊耕（RT）和条耕（ST）相比，免耕条件下的产量并非最佳，后两者的产量均高于传统耕作方式。原始数据展示了从符合纳入标准的同行评审文章中提取的每个参数和数据，而分析数据则是通过拟合随机和混合效应模型进行的元分析所获得的数据。

原始数据
在此，数据包括从符合纳入标准的同行评审文章中提取的所有参数和变量。此外，还有一个包含所有缩写及其含义的元数据表。

数据分析
在此，估计值、下限（ci.lb）和上限（ci.ub）置信区间均以对数转换格式呈现。因此，取指数得到反应比。Se = 标准误，zval = z相关值，pval = p值，RT（脊耕）和RH（数量）= 脊高。C = 棉花，Cr = 胡萝卜，RD = 菊苣，T = 郁金香，GM = 玉米，WC = 冬季谷物，SB = 甜菜，SC = 春季谷物，P = 马铃薯。此外，还有一个包含所有缩写及其含义的元数据表。

图形
图 1. 表现出基于随机效应模型估计的平均反应比（RR）的森林图，展示了免耕（NT）、脊耕（RT）和条耕（ST）的数据点。n 是特定耕作类型的样本量/研究数量。误差线代表 95% 置信区间 CI。正方形代表估计的反应比，其大小对应其权重（基于其样本量）。垂直虚线代表无效应线。