Fig 1.
Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Flow Chart describing the protocol used for searching, identifying, and selecting publications for the current meta-analysis.
‘n’ represents the total number of studies, or for the specific analyses if specified (yield, C-SOC, and N-SOC).
Table 1.
Overview of the data set used for the YIELD and SOC meta-analyses, with columns for the study*country*site combination index (id), study (reference), aridity index class (AI class)*, time span of the study’s experiment (time span), number of yield data (nYield), number of SOC data associated with C and N rates (nSOC C input and nSOC N input, respectively) (*) The site’s aridity index was extracted from the CGIAR-CSI Global-Aridity and Global-PET Database [51].
Table 2.
List of organic resource types and associated quality classes, with columns for the respective number of studies (nReferences), number of yield data (nYield), and number of SOC data with C and N rates different from zero (nSOC C input and N input, respectively).
Fig 2.
Graph illustrating conceptual response curves of a mineral, organic, and combined input treatment with substitutive design (50% mineral, 50% organic) with interactive effects that can be (a) positive, (b) none existent, and (c) negative.
Fig 3.
Distribution of data points, represented by scaled count data for each subtreatment across total N rate, applied as mineral or organic fertilizer.
Fig 4.
(left) Overview of reported maize grain yield (t ha-1) data, averaged by publication, site, and treatment, and ranked following increasing ORMR yields.
Error bars represent the +- 1.96 * mean standard error on that average. The overall yield average by treatment and across publications and sites is labeled as ‘summary measure’ at the bottom of the graph. (top right) Distribution of maize grain yield (t ha-1) values by treatment.
Fig 5.
The agronomic efficiency (AE, in kg kgN-1) across a total N input range of 0-200 (kg ha-1), applied as mineral or organic fertilizer, and segregated by treatment and organic input quality class.
Fig 6.
Model predicted maize grain yield values were plotted for the available total N rate, within a range of 0-200 (kg ha-1), applied as mineral or organic fertilizer, separately for each organic class and ORMR with three different organic N rate proportions.
The root mean square error (RMSE, in t ha-1) is given for the different predicted yields from treatments with the respective organic classes.
Fig 7.
The agronomic efficiency (AE, in kg kgN-1) was plotted for the available total N rate, within a range of 0-200 (kg ha-1), applied as mineral or organic fertilizer, separately for each organic class and for ORMR with three different organic N input proportions.
Fig 8.
Yield standard deviation (SD) responses across time are shown for each subtreatment, and relative to their respective predicted yield values.
Values are computed based on the random variances across time from the YIELD and individual organic class models.
Fig 9.
The output of the N-SOC model is used to predict changes in soil organic carbon from its initial values (dSOC) across the cumulative total N input rates (t ha-1), applied as mineral or organic fertilizer and for which data are available, but limited to five cumulative t N ha-1.
They are plotted at a data base average initial SOC of 1.8% and separately for each organic class and ORMR with different proportions of organic N content. The root mean square error (RMSE, in %) is given for the different predicted dSOCs from treatments with the respective organic classes.
Fig 10.
The output of the C-SOC model is used to predict changes in soil organic carbon from its initial values (dSOC) across cumulative organic carbon input rates.
These are then plotted for each treatment and organic class, and at an initial SOC of 1.8% and cumulative mineral N input of 750 kg ha-1. The 750 kg ha-1 was based on an annual mineral N application of 75 kg during a time frame of 10 years. The root mean square error (RMSE, in %) is given for the dSOC predictions of the different subtreatments.