# Influence of cover crops and their traits on the yield of main annual crops grown in tropical and subtropical environments - A meta-analysis

Demayi Jores Mauryo Fassinou, Pierrot Lionel Yemadje, Mathias Christina, Hervé Nonwègnon Sayimi Aholoukpé, Benjamin Heuclin, Kpedetin Hermione Koussihouede, Pablo Tittonell

► To cite this version:

Demayi Jores Mauryo Fassinou, Pierrot Lionel Yemadje, Mathias Christina, Hervé Nonwègnon Sayimi Aholoukpé, Benjamin Heuclin, et al. Influence of cover crops and their traits on the yield of main annual crops grown in tropical and subtropical environments - A meta-analysis. Field Crops Research, 2026, 335, pp.110201. 10.1016/j.fcr.2025.110201. hal-05329047

HAL Id: hal-05329047

https://hal.science/hal-05329047v1

Submitted on 24 Oct 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

# Influence of cover crops and their traits on the yield of main annual crops grown in tropical and subtropical environments – A meta-analysis

Demayi J. Mauryo Fassinou<sup>a,c,g,h,i,*</sup>, Pierrot Lionel Yemadje<sup>a,b,c,*</sup>, Mathias Christina<sup>c,d</sup>, Hervé S. Nonwègnon Aholoukpè<sup>f</sup>, Benjamin Heuclin<sup>c,d</sup>, Hermione Koussihouèdé<sup>e</sup>, Pablo Tittonell<sup>c,j</sup>

## ARTICLE INFO

**Keywords:**

Ecological intensification

Functional traits

Service crops

Companion crops

Crop association

Cover plant

## ABSTRACT

**Context or problem:** The sustainability of agriculture in tropical regions is threatened by climate change, soil degradation, and food insecurity. Crop diversification through growing cover crops is a promising strategy to mitigate these problems. However, their effects in rotation and as intercrops has not been comprehensively assessed in the tropics, considering cultural practices, edapho-climatic conditions, and the functional traits of these cover crops.

**Objective or research question:** This study aims to conduct a comprehensive meta-analysis to simultaneously analyze, in rotation and intercropping systems, the effect of cover crops and their traits on the yield of main annual crops grown in tropical and subtropical regions.

**Methods:** A meta-analysis was conducted based on 1788 field observations from 191 articles covering 29 tropical and subtropical countries and regions. The analysis accounted for both rotation and intercropping systems, while considering the variability in cultural practices, edapho-climatic conditions, and traits of cover crops.

**Results:** The results showed that the cover crops increased yields of cereals, legumes, and fiber crops by 25% in rotation and by 7% in intercrops compared to monocrops. These effects varied greatly depending on the context. In rotation, yield gains were enhanced by crop residue incorporation, nitrogen application, bimodal tropical climates compared to unimodal ones, and altitude. In intercrops, positive effects were associated with the use of Fabaceae as cover crops, staggered sowing, altitude, temperature, and subtropical climates and volcanic soils (Andosols). Yield responses varied depending on the annual crop: in rotation, maize (290%), soybeans (300%), and cotton (240%), relative yield gains were observed in low-yielding environments, while in intercrops only maize (360%) showed gains under these conditions, and rice (-30%) suffered a decline. Analysis of cover crop traits revealed that in rotation systems, aboveground cover crops biomass, root diameter and root density explained part of the relative yield variability and were positively associated with relative yield. In intercrops, cover crops aboveground nitrogen mass was explanatory of annual crop yield.

**Conclusions:** Fabaceae cover crops tend to perform better in intercropping, particularly in subtropical climates and in Andosols. In tropical crop rotations, conserving cover crop residues improves yields, further enhanced by nitrogen fertilization. Cover crops are particularly beneficial in low-yield contexts. Furthermore, the traits of cover crops are insufficiently documented, and their explanatory power remains limited, making the effects of

<footnote><sup>a</sup> Institute of Cotton Research (IRC), 01 PO Box 175, Cotonou, Benin</footnote>

<footnote><sup>b</sup> CIRAD, UPR AIDA, Cotonou, Benin</footnote>

<footnote><sup>c</sup> AIDA, Univ Montpellier, CIRAD, Montpellier, France</footnote>

<footnote><sup>d</sup> CIRAD, UPR AIDA, Montpellier F-34398, France</footnote>

<footnote><sup>e</sup> CIRAD, UMR ABSys, Montpellier F-34398, France</footnote>

<footnote><sup>f</sup> Institut National des Recherches Agricoles du Bénin, Benin</footnote>

<footnote><sup>g</sup> Faculty of Agronomic Sciences, University of Abomey-Calavi, Cotonou, Benin</footnote>

<footnote><sup>h</sup> Soil Sciences Laboratory, Cotonou, Benin</footnote>

<footnote><sup>i</sup> UR Ecopedology, University of Abomey-Calavi, Cotonou, Benin</footnote>

<footnote><sup>j</sup> Groningen Institute of Evolutionary Life Sciences, Groningen University, P.O. Box 11103, Groningen 9700 12 CC, the Netherlands</footnote>

<footnote>* Corresponding authors at: CIRAD, UPR AIDA, Cotonou, Benin
E-mail addresses: mauryo.fassinou@gmail.com (D.J. Mauryo Fassinou), pierrot-lionel.yemadje@cirad.fr (P.L. Yemadje).</footnote>
cover crops on annual crop yields unclear. This highlights the importance of conducting further research in this area to gather additional evidence.

# 1. Introduction

Climate change, declining soil fertility, and food insecurity pose significant challenges to agricultural sustainability, particularly in tropical and subtropical regions (Small and Raizada, 2017; Eze et al., 2020; Ortiz-Bobea et al., 2021). In response to these challenges, agricultural systems based on plant diversification are increasingly recognized as promising strategies for ensuring sustainable agricultural production (Munir and Bashir, 2019; Altieri and Nicholls, 2020). Within these diverse systems, cover crops play a crucial role. They can be integrated either through rotation, where cover crops and other species are grown sequentially on the same plot over the seasons, or through intercropping, where cover crops and other species are grown simultaneously on the same plot in the same season (Naudin et al., 2015; Shekhar et al., 2024). In both cases, cover crops provide multiple ecosystem services, including improving soil structure and porosity, sequestering carbon, regulating biogeochemical cycles, suppressing weeds, conserving water, reducing erosion, and improving crop yields (Snapp et al., 2005; Acharya et al., 2019; Daryanto et al., 2019; Blanco-Canqui and Ruis, 2020; Garba et al., 2022). The growing interest in integrating them into tropical and subtropical agroecosystems is based on their potential to respond to the specific constraints of these environments (Kinyua et al., 2019). For example, cover crops represent a promising strategy for enhancing soil health, mitigating erosion, sequestering carbon, and improving the resilience of tropical agricultural systems to climate change (Delgado et al., 2021). Several recent meta-analyses have highlighted the benefits of cover crops in these regions by separately evaluating their effects in rotation systems (Abdalla et al., 2019; Peng et al., 2024; He et al., 2025) or in intercropping systems (Osipitan et al., 2018). These studies also emphasize the importance of cultural practices and edapho-climatic conditions in modulating the effects of cover crops. These studies have also explored the impact of these plants on the yields of several annual crops individually without cross-referencing these effects with the type of system (rotation or intercropping) in the same study. However, assessing rotation and intercropping systems in separate studies does not allow for a direct comparison of cover crop effects between the two systems. It thus reduces the possibility of identifying the contexts in which one or the other cover crop species would be most suitable.

Furthermore, while the overall benefits of cover crops in cropping systems are widely documented, as are their negative effects when poorly managed (Lobell et al., 2025), the specific ways in which their traits influence annual crop yields remain underexplored in tropical; and subtropical contexts. While plant families such as grasses, legumes, and brassicas are often used to guide cover crop species selection, trait-based approaches are increasingly recognized as powerful tools for understanding and managing plant diversity from an agroecological perspective (Martin and Isaac, 2015, 2018; Wood et al., 2015; Wagg et al., 2021). Traits, as indicators of an organism's responses to or effects on its environment, provide an explanatory framework to understand how plant diversity influences ecosystem functioning and services (Lavorel and Garnier, 2002). They can also serve as a generalizable basis for comparing and predicting ecosystem responses to different environmental and management conditions (Garnier and Navas, 2012). Despite some studies examining the effect of specific cover crop traits on annual crop yields (Chikoye and Ekeleme, 2003; Borges et al., 2022; E Silva et al., 2024), no comprehensive synthesis has yet been proposed. However, such a synthesis is necessary, as the effectiveness of cover crops can vary considerably based on their morphological, physiological, and biochemical attributes, such as the type and depth of their root system (Pisarcik et al., 2024), the quantity and quality of biomass

produced (Finney et al., 2016), their ability to fix atmospheric nitrogen (Barbieri et al., 2023), or release allelopathic compounds (Jabran et al., 2015).

A comprehensive meta-analysis that incorporates both rotation and intercropping systems within tropical and subtropical agroecosystems is deemed necessary, taking into account diverse cultural practices and edapho-climatic conditions. This approach would not only enable a comparison between alternative cropping systems but also help to clarify how cover crop traits may influence annual crop yields. This knowledge is crucial for optimizing the selection and management of cover crops to enhance agricultural sustainability in these regions. The objective of this article is to simultaneously analyze crop rotation and intercropping systems, based on scientific literature, in order to assess the effects of cover crops on annual crop yields in tropical and subtropical contexts, considering cultural practices, edapho-climatic conditions, and cover crop traits. Thus, the research questions examined are as follows:

(i) How do cover crops influence annual crop yields depending on whether they are used in rotation or as intercrops? and,

(ii) What role do cover crop traits play in explaining the variability in annual crop yields?

# 2. Materials and methods

## 2.1. Literature search

A comprehensive search was conducted for peer-reviewed publications on the effect of cover crop (CC) traits on annual crop yields in rotation and intercropping systems under tropical and subtropical agroecosystems using the Scopus database (Elsevier). The following search terms were used (an asterisk replacing any end of the respective term): ('cover crop*' OR 'cover plant*' OR 'companion plant*' OR 'intercrop*' OR 'mixed crop*' OR 'companion crop*' OR 'crop* association' OR 'associated crop*') AND (tropic* OR subtropic*) in the title of the publication, abstract or keywords. Database searches have been increased with library resource searches to find relevant articles based on citations in retrieved articles. The search was last updated in March 2025. A total of 2296 unique publications were identified based on title and abstract. A second selection stage was then conducted based on a full-text assessment, resulting in 1250 publications.

## 2.2. Eligibility criteria

The studies included in the meta-analysis had to meet the following selection criteria: (i) the study had to have been carried out in field conditions; (ii) the experimental design had to be specified and the number of repetitions had to be at least equal to three; (iii) the study had to compare either monocropping within intercropping or fallow with crop rotation, with similarly managed treatments; (iv) grain yield data, including controls, had to be available; (v) studies with simulated yield data were excluded; (vi) full-text articles had to be accessible. The literature search and selection procedure are presented in a Prisma diagram (Appendix A, Fig. A1). A total of 191 articles published between 1971 and 2025, with no year restrictions, from twenty-nine countries (Fig. 1) met our selection criteria for the meta-analysis (Table A1). The resulting database comprised 191 experiments and 1788 observations, comparing either monoculture with intercropping (1288 observations) or fallow with a crop rotation (500 observations).
## 2.3. Data compilation

Yield data for different annual crop species reported in the 191 selected publications were extracted. We also compiled information on several studies and site descriptors that may have influenced yield response in intercropping and crop rotation (Table 1). First, the geographical coordinates of the experimental sites were recorded. The Köppen climate classification (Kottek et al., 2006) was then assigned to each site using the Köppen-Geiger world map. Climate types were grouped into four broad categories: tropical (Aw, Af, Am, and As), subtropical (Cfa, Cwb, Cwa, and Csa), semi-arid (BSh and BSk), and oceanic (Cfb) (Viaud et al., 2023). Rainfall regimes were classified into two types: unimodal and bimodal. When this information was not explicitly indicated in the publication, it was inferred from the site's climate data. Additionally, the type of trial was categorized according to its location: on-farm or on-station. Similarly, the tillage method was classified into three categories: tillage, minimum-tillage, and no-tillage. Other environmental variables were included, such as site altitude (m), average annual temperature (°C), rainfall during the experiment, and average annual rainfall (mm). When these data were not available in the published literature, they were estimated from the NASA Prediction Of Worldwide Energy Resources (POWER) database via the Data Access Viewer (NASA POWER 2025), based on the geographical coordinates of the sites. If the latitude, longitude, and/or altitude coordinates were not specified, they were obtained using Google Maps, referring to the name of the study site. The presence or absence of irrigation was also noted to compare the performance of strictly rainfed systems with those that received supplementary irrigation. In addition, variables related to the

initial soil characteristics were collected, including cation exchange capacity (mmol(c)/kg), base saturation (%), texture, bulk density (g cm⁻³), and the content of clay, silt, and sand (%). An aggregate variable corresponding to the sum of fine soil particles (clay + silt) was also calculated. Soil textures were grouped into three classes according to the USDA textural classification (Tonitto and Ricker-Gilbert, 2016; Ouedraogo et al., 2020; Allakonon et al., 2022): light soils (sandy, sandy loam, silty sand, sandy clay loam); medium soils (loamy, clayey silt, sandy clay); and heavy soils (clay, clay loam, silt, silty clay, clayey silt loam). Soil types were recorded according to the WRB classification, with those with a small number of observations (<50) placed in the “other” category. To assess the potential effect of nutrient substitution by CC, we considered the application or non-application of nitrogen fertilizer for both annual crops and CC. Another binary variable was the presence or absence of agroforestry in the system. The number of CC used in each experiment was recorded, along with their planting method (whether mixed or monoculture). CC grown in intercropping or rotation with annual crops were grouped into four families: Asteraceae, Brassicaceae, Fabaceae, and MixtFamily (when a mixture of several families was used). When a mixture of CC belonged to a single family, it was classified under the name of that family, while inter-family mixtures were categorized as MixtFamily. Similarly, annual crops were grouped according to their botanical origin: legumes (Glycine max, Phaseolus vulgaris, Vigna unguiculata, Vigna radiata, Dolichos lablab, Cicer arietinum, and Vicia faba), cereals (Hordeum vulgare, Oryza sativa, Pennisetum glaucum, Sorghum bicolor, Triticum aestivum, Triticum sativum, Zea mays, Eragrostis tef, Pennisetum typhoides, and Eleusine coracana), oilseeds (Sesamum indicum, Arachis hypogaea, and Helianthus annuus), and fiber

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Location of experiments included in this meta-analysis. All experiments come from studies comparing yields with and without cover crops in intercropping and rotation systems. Map created using ArcGIS 10.4 software.</figcaption></figure>
<table><caption>**Table 1**

Description of the explanatory variables included in the meta-analysis.</caption><thead><tr><th>Systems</th><th>Variables</th><th>Definition</th><th>Categories</th></tr></thead><tbody><tr><td rowspan="14">Both systems</td><td>Climate type</td><td>The climate of the experimental site is classified by Köppen Classification (Kottke et al., 2006)</td><td>(i) Tropical, (ii) Semi-arid, (iii) Subtropical, and (iv) Oceanic</td></tr><tr><td>Rainfall Type</td><td>Rainfall distribution over the year</td><td>(i) Unimodal and (ii) Bimodal</td></tr><tr><td>Study Type</td><td>Location of the experimental trial</td><td>(i) On-farm and (ii) On-station</td></tr><tr><td>Tillage type</td><td>Tillage practices are applied throughout any phase of the rotation and intercropping.</td><td>(i) Tillage and (ii) Minimum-tillage (ii) No-tillage</td></tr><tr><td>Actual Rainfall</td><td>Average rainfall during the experiment</td><td>Unclassified continuous numeric variable</td></tr><tr><td>Average Temperature (°C)</td><td>Average annual temperature at the experimental site</td><td>Unclassified continuous numeric variable</td></tr><tr><td>Elevation (m)</td><td>Height of the experimental plot in relation to sea level</td><td>Unclassified continuous numeric variable</td></tr><tr><td>Average Rainfall (mm)</td><td>Average annual rainfall throughout the experiment</td><td>Unclassified continuous numeric variable</td></tr><tr><td>Irrigation status</td><td>Cover crop or annual crop grown under full rainfall or supplementary irrigation systems</td><td>(i) Irrigation and (ii) No irrigation</td></tr><tr><td>Clay content (%)</td><td>The clay content of the soil at the experimental site</td><td>Unclassified continuous numeric variable</td></tr><tr><td>Silt content (%)</td><td>The silt content of the soil at the experimental site</td><td>Unclassified continuous numeric variable</td></tr><tr><td>Sand content (%)</td><td>The sand content of the soil at the experimental site</td><td>Unclassified continuous numeric variable</td></tr><tr><td>Clay content + Silt content (%)</td><td>The sum of fine soil particles</td><td>Unclassified continuous numeric variable</td></tr><tr><td>Organic matter (%)</td><td>The initial organic matter content of the soil at the experimental site</td><td>Unclassified continuous numeric variable</td></tr><tr><td>Cation exchange capacity (mmol<sub>c</sub>.kg<sup>-1</sup>)</td><td>The initial cation exchange capacity of the soil at the experimental site</td><td>Unclassified continuous numeric variable</td></tr><tr><td>Base saturation (%)</td><td>The initial base saturation of the soil on the experimental site</td><td>Unclassified continuous numeric variable</td></tr><tr><td>Texture</td><td>The initial relative proportion of mineral particles in the soil at the experimental site</td><td>(i) Light soils, (ii) Medium soils, and (iii) Heavy soils</td></tr><tr><td>Soil type</td><td>Soil classification according to WRB</td><td>(i) Andosol, (ii) Cambisol, (iii) Luvisol, (iv) Acrisol, (v) Ferralsol, (vi) Vertisol, and (vii) others: Alfisol, Fluvisol, Gleysol, Leptosol, Lixisol, Nitisol, Phaeozem, Planosol, Plinthosol, Regosol, Ultisol</td></tr><tr><td rowspan="10">Rotation</td><td>Soil bulk density (g cm<sup>3</sup>)</td><td>Mass of dry soil per unit volume of soil on the experimental site</td><td>Unclassified continuous numeric variable</td></tr><tr><td>N fertilizer AC</td><td>A binary factor indicating whether N fertilizer was applied to the annual crop</td><td>(i) Fertilized and (ii) unfertilized</td></tr><tr><td>N fertilizer CC</td><td>A binary factor indicating whether N fertilizer was applied to the cover crop</td><td>(i) Fertilized and (ii) unfertilized</td></tr><tr><td>N fertilizer rate AC (Kg. ha<sup>-1</sup>)</td><td>The rate of N fertilizer applied to the annual crop</td><td>Unclassified continuous numeric variable</td></tr><tr><td>N fertilizer rate CC (Kg. ha<sup>-1</sup>)</td><td>The rate of N fertilizer applied to the cover crop</td><td>Unclassified continuous numeric variable</td></tr><tr><td>Agroforestry present</td><td>Binary factor indicating whether agroforestry is present</td><td>(i) Agroforestry and (ii) No agroforestry</td></tr><tr><td>CC functional type</td><td>Cover crop plant family with distinguished functional traits.</td><td>(i) Asteraceae, (ii) Brassicaceae, (iii) Fabaceae and (iv) Poaceae</td></tr><tr><td>Annual crop types</td><td>Grouping of annual crop species in experiments according to their botanical characteristics</td><td>(i) Legumes, (ii) Cereals, (iii) Oilseed, and (iv) Fiber</td></tr><tr><td>Number of cover crops involved</td><td>The number of CC used in each experiment</td><td>Unclassified discontinuous numeric variable</td></tr><tr><td>Annual crop combined with a cover crop before rotation</td><td>Association of CC with an annual crop before rotation</td><td>(i) Combined and (ii) uncombined</td></tr><tr><td>Cutting date cover crops (month)</td><td>Duration of the cover crop before sowing the annual crop</td><td>Uncategorized numerical variable</td></tr><tr><td rowspan="3">Intercropping</td><td>Biomass management</td><td>The fate of CC residues post-termination</td><td>(i) Exported, (ii) Left on soil, and (iii) Incorporated</td></tr><tr><td>Spatial arrangement CC</td><td>The spatial arrangement of the cover crop with the annual crop</td><td>(i) Inter-row, (ii) Broadcast, (iii) On annual crop lines, and (iv) Strip sowing</td></tr><tr><td>CC sowing time</td><td>Time of insertion of CC into CA</td><td>(i) At the same time, (ii) After and (iii) Before</td></tr></tbody></table>

plants (Gossypium sp. and Gossypium hirsutum). Variables specific to intercropping systems were also taken into account: the arrangement of CC with annual crops (inter-row, broadcast, on annual crop rows, or in strips) and the timing of CC sowing relative to the annual crop ("before" when sown before the annual crop, "simultaneously," or "after" when sown later). For rotation systems, specific variables included cover crop cycle duration (in months), biomass management after harvest (exported, left on the soil, or incorporated into the soil), and a binary variable indicating whether the cover crop had previously been intercropped with an annual crop. Information on these variables was extracted from

texts, tables, or figures using the online image analysis tool Web-PlotDigitizer v4.8 (Rohatgi, 2020). The values of the explanatory variables were considered missing values when it was not possible to extract the information. For variables with less than 20 % missing values, imputation was performed by principal component analysis using the *mice* function of the *mice* R package (Josse and Husson, 2016). Conversely, some variables initially considered were excluded due to excessive missing values. The definition and units of all explanatory variables selected are presented in Table 1.

To study CC traits, all parameters measured directly on CC,
<table><caption>**Table 2**
Description of cover crop traits extracted from experimental trials.</caption><thead><tr><th>Type</th><th>Traits</th><th>Definition</th></tr></thead><tbody><tr><td rowspan="20">Aboveground</td><td>Aboveground dry mass (Kg ha<sup>-1</sup>)</td><td>Dry biomass of all living vegetation aboveground biomass</td></tr><tr><td>Total dry mass (Kg ha<sup>-1</sup>)</td><td>Dry biomass of all living vegetation belowground biomass</td></tr><tr><td>Dry matter content (g kg<sup>-1</sup>)</td><td>Mass of plant tissue after removal of moisture</td></tr><tr><td>Height (m)</td><td>Vertical distance between the soil surface and the top of the cover crop canopy</td></tr><tr><td>Ground cover (%)</td><td>Percentage of soil surface shaded or protected by cover crop vegetation</td></tr><tr><td>Leaf area index (m<sup>-2</sup> m<sup>-2</sup>)</td><td>Total leaf area per square meter of ground surface</td></tr><tr><td>Leaf Stem ratio</td><td>The ratio between the dry mass of the leaves and that of the stems</td></tr><tr><td>Leaf blade/stem ratio</td><td>The dry mass of the leaf blade is divided by the dry mass of the stem.</td></tr><tr><td>Tille density (m<sup>2</sup>)</td><td>Number of tillers per square meter</td></tr><tr><td>Leaf appearance rate (leaves/days)</td><td>Number of new leaves produced by the cover crop per day</td></tr><tr><td>Leaf elongation rate (cm/days)</td><td>Rate of increase in cover crop leaf length per day</td></tr><tr><td>Stem elongation rate (cm/days)</td><td>Rate of increase in cover crop stem length per day</td></tr><tr><td>Phyllochiron (days/leaves)/Tillers</td><td>The interval between the appearance of two consecutive leaves on the cover crop</td></tr><tr><td>Leaf senescence rate (cm/days)</td><td>The speed at which senescent leaves regress in size or disappear</td></tr><tr><td>Leaf blade length (cm/days)</td><td>Length of CC leaf blade</td></tr><tr><td>Leaf number (leaves)</td><td>Number of living leaves on each tiller</td></tr><tr><td>Branch number</td><td>Total number of branches</td></tr><tr><td>PAR (umol/m<sup>2</sup>)</td><td>Photosynthesis rate of aboveground biomass</td></tr><tr><td>Aboveground N content (Kg ha<sup>-1</sup>)</td><td>Quantity of nitrogen present in the aboveground biomass</td></tr><tr><td>Aboveground P content (Kg ha<sup>-1</sup>)</td><td>Quantity of phosphorus present in the aboveground biomass</td></tr><tr><td>Aboveground K content (Kg ha<sup>-1</sup>)</td><td>Quantity of potassium present in the aboveground biomass</td></tr><tr><td>Aboveground C content (Kg ha<sup>-1</sup>)</td><td>Quantity of carbon present in the aboveground biomass</td></tr><tr><td>Aboveground S content (Kg ha<sup>-1</sup>)</td><td>Quantity of sulfur present in the aboveground biomass</td></tr><tr><td>Aboveground Ca content (Kg ha<sup>-1</sup>)</td><td>Quantity of calcium present in the aboveground biomass</td></tr><tr><td>Aboveground Mg content (Kg ha<sup>-1</sup>)</td><td>Quantity of magnesium present in the aboveground biomass</td></tr><tr><td>Aboveground Mn content (Kg ha<sup>-1</sup>)</td><td>Quantity of manganese present in the aboveground biomass</td></tr><tr><td>Aboveground Fe content (Kg ha<sup>-1</sup>)</td><td>Quantity of iron present in the aboveground biomass</td></tr><tr><td>Aboveground Zn content (Kg ha<sup>-1</sup>)</td><td>Quantity of zinc present in the aboveground biomass</td></tr><tr><td>Aboveground B content (Kg ha<sup>-1</sup>)</td><td>Quantity of boron present in the aboveground biomass</td></tr><tr><td>Aboveground Cu content (Kg ha<sup>-1</sup>)</td><td>Quantity of copper present in the aboveground biomass</td></tr><tr><td>Aboveground CN ratio</td><td>Proportion of carbon (C) to nitrogen (N) in the aboveground biomass</td></tr><tr><td>Lignin N ratio (lig/N)</td><td>Relative quantity of lignin in relation to total nitrogen in an aboveground biomass</td></tr><tr><td>Grain yield (Kg ha<sup>-1</sup>)</td><td>The total quantity of grain produced</td></tr><tr><td>Grain dry weight (g)</td><td>The average weight of 1000 grains</td></tr><tr><td>Pods per plant</td><td>Number of pods produced by a single plant</td></tr><tr><td>Seeds per pod</td><td>Number of seeds in a pod</td></tr><tr><td>Fresh pod number</td><td>Total number of fresh pods</td></tr><tr><td>Pod fresh mass (g)</td><td>The total mass of pods produced by a plant</td></tr><tr><td>Root length density (cm/cm<sup>3</sup>)</td><td>The total amount of root length measured in a given volume of soil</td></tr><tr><td>Nodules fresh mass weight (g)</td><td>Total mass fresh root nodules</td></tr><tr><td>Nodules number</td><td>Total number of nodules formed on the roots</td></tr><tr><td>Root diameter (mm)</td><td>The average transverse thickness of the root</td></tr><tr><td>Root N content (Kg ha<sup>-1</sup>)</td><td>Quantity of nitrogen present in the total root biomass</td></tr><tr><td>Root CN ratio</td><td>Ratio of carbon (C) content to total nitrogen (N) content in root biomass</td></tr></tbody></table>

All traits are continuous numerical variables

regardless of their type or purpose, were considered traits. These are morphological, physiological, and phenological characteristics, measurable at the plant level, that have an impact on plant performance (Violle et al., 2007; Damour et al., 2014). Data relating to these traits were systematically extracted from the text, tables, or figures. When information was presented in graphical form, it was digitized using WebPlotDigitizer v4.8 software (Rohatgi, 2020). No restrictions were imposed on the nature of the traits measured. Trait values were considered missing when it was not possible to extract the information. Units of measurement were harmonized to allow comparison between studies. The definitions and units of all selected CC traits are presented in Table 2.

## 2.4. Meta-analysis

### 2.4.1. Linear mixed model

Two mixed-effects models were constructed to estimate the effect of cover crops and explanatory variables on annual crop yields, each applied to a distinct subset of the database: (1) Model 1, applied to the dataset from intercropping systems; (2) Model 2, applied to the dataset from crop rotation systems. The yields used in this meta-analysis were grain yields of annual crops. The methodological approach used to construct the models is detailed in the following sections.

### 2.4.2. Effect size and weight of the observations

The Napierian logarithm of the annual crop yield ratio between diversified cropping system (DC, i.e., rotation or intercropping) and control treatment (CT, i.e., monocropping) was used as the effect size in the meta-analysis (Hedges et al., 1999):

$$L = \frac{\text{YieldDC}}{\text{YiedCT}} \qquad (1)$$

Where YieldDC and YieldCT are the annual crop yields in the intercropping (or rotation) and control treatments, respectively. The results were back-transformed and presented as the percentage change in annual crop yield of the DC treatment compared to the CT treatment in the figures and results. The number of replicates was used to weight the observations, ensuring robustness and statistical significance by maintaining a sufficient sample size (Viaud et al., 2023). The weight formula used was as follows:

$$W_{L_i} = \frac{n * r_i}{\Sigma_j r_j} \qquad (2)$$

Where WLᵢ is the weight of observation i, n is the total number of observations, rᵢ is the number of repetitions of observation i, and Σⱼrᵢ the sum of repetitions. In meta-analyses, observations were generally weighted by the inverse of their variance. Unfortunately, this
information was missing from many of the selected studies.

### 2.4.3. Sensitivity and publication bias analysis

An initial exploration revealed that the intercropping (Fig. A2) and rotation (Fig. A3) data sets exhibited a non-Gaussian residual distribution. To correct this, observations with residues higher than 1.96 or lower than -1.96 were removed. Then, observations with a Cook distance greater than 4/n were removed from the dataset (where n is the number of observations) (Viechtbauer, 2010). A new bibliographic dataset was then extracted for both systems. Finally, the complete datasets contained 1053 observations for the intercropping systems and 424 observations for the rotation systems. Publication bias was assessed by drawing funnel plots, in which standard errors were plotted against the log of the effect size ratio (Fig. A6 and Fig. A7, Duval and Tweedie, 2000). Publication bias was assessed using data with a reliable coefficient of variation (CV) greater than 5%. The asymmetry of the funnel plot was tested using a rank correlation test (Begg and Mazumdar, 1994).

### 2.4.4. Data analysis

The top-down strategy was used as a reference for data processing in this meta-analysis (Zuur et al., 2009). We start using the beyond optimal model including all explanatory variables and 'same_control' factor (grouping observations that received the same control treatment for a given year and site) nested with the 'experience' factor (corresponding to a set of CT observations from the same site over several years) as grouping random effect. These effects were introduced to model dependencies between observations within a single study (Lajeunesse, 2011). The model is given by:

$$L_{ijk} = \mu + (\sum_m a_m X^{m}_{ijk}) + b_j + c_k + \varepsilon_{ijk} \quad (3)$$

Where $L_{ijk}$ is the effect size for observation j from the 'same_control' i in experience k, $\mu$ is the intercept, $X^{m}_{ijk}$ is the m explanatory variable with its coefficient $a_m$, $b_i$ is the random effect ('same_control'), $c_k$ is the random effect ('experience') and $\varepsilon_{ijk}$ the residual.

The top-down strategy consists in determining the optimal structure of random effects by model comparison using Akaike Information criterion (Akaike, 1998). For this step, the fixed structure is the same for all models, only the random structure differs. The restricted maximum likelihood (REML) estimation was used. Then the optimal structure of fixed effect is also determined by model comparison using Fisher test.

The restricted maximum likelihood (REML) estimation was used.

The best models are presented in Table 3.

The validity of the assumptions of homogeneity of variances and normality of residuals was assessed graphically using histograms of residuals, quantile-quantile plots (QQ-plots) of Pearson residuals compared with a standardized normal distribution, and plots of residuals against fitted values (Figs. A4 and A5).

All statistical analyses were performed using R software (R. Core Team, 2025). Linear mixed models were fitted using the *lme* function in the *nlme* package (Pinheiro et al., 2021). Multiple comparisons for categorical variables were performed via the *emmeans* function of the *emmeans* package (Lenth et al., 2018).

## 2.5. Complementary analysis on annual crop species and cover crop traits

Due to the limited number of observations, the annual crop yield response at the crop species level and the annual crop yield response to cover crop traits could not be tested through the meta-analysis. Therefore, complementary analyses were performed using a similar mixed linear model to the one used in the meta-analysis, with the same random effect, and the observations retained after Cook's distance analysis. The change in DC-to-CT yield ratio with the CT yield as fixed effect, and random effects previously mentioned, was tested for each crop species with more than 10 observations. Considering cover crop traits, the response in log yield ratio to cover crop traits was tested with the same type of model with the cover trait value as a fixed effect. One model per cover crop trait was tested when the number of observations was higher than 10. The same R functions and packages described previously were used.

# 3. Results

## 3.1. Variation in the effect of cover crops on annual crop yields

The studies retrieved from the literature and included in the meta-analysis originated from 30 different countries, most of them from sub-Saharan Africa (Fig. 1). Cropping practices and edapho-climatic conditions modulated the effect of cover crops on annual crop yields (i.e., the DC:CT yield ratio) in intercropping and rotation systems, as shown in Table 3.

<caption>**Table 3**</caption>

Analysis of variance (ANOVA) of the random mixed-effects models built to assess the influence of explanatory variables on the DC-to-CT relative yield for the two models. The degree of freedom (df), p-values, and F-values are indicated. Boxes left blank indicate variables not selected based on the stepwise regression.

<table><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="3">Crop rotation</th><th colspan="3">Intercropping</th></tr><tr><th>df</th><th>F</th><th>p</th><th>df</th><th>F</th><th>p</th></tr></thead><tbody><tr><td rowspan="5">Climate</td><td>Type II ANOVA</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Intercept</td><td>1261</td><td>0.34</td><td>0.5619</td><td>1478</td><td>1.65</td><td>0.1995</td></tr><tr><td>Climate type</td><td>2,52</td><td>4.57</td><td>0.0148</td><td>3443</td><td>3.76</td><td>0.0109</td></tr><tr><td>Rainfall type</td><td>1,52</td><td>4.12</td><td>0.0475</td><td></td><td></td><td></td></tr><tr><td>Altitude</td><td>1261</td><td>5.20</td><td>0.0234</td><td>1443</td><td>50.57</td><td>&lt; 0.0001</td></tr><tr><td rowspan="7">Management</td><td>Average temperature</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Average rainfall</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Soil tillage</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Irrigation</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>N application on the main crop</td><td>1261</td><td>4.99</td><td>0.0263</td><td></td><td></td><td></td></tr><tr><td>N application on cover crop</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CC biomass management</td><td>2261</td><td>4.28</td><td>0.0149</td><td>x</td><td>x</td><td>x</td></tr><tr><td rowspan="3">CC characteristics</td><td>CC spatial arrangement</td><td>x</td><td>x</td><td>x</td><td></td><td></td><td></td></tr><tr><td>CC sowing time</td><td>x</td><td>x</td><td>x</td><td>2478</td><td>18.79</td><td>&lt; 0.0001</td></tr><tr><td>Number of CC</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="2">Soil</td><td>Cover crop mixture</td><td></td><td></td><td></td><td>1478</td><td>3.68</td><td>0.0557</td></tr><tr><td>CC family</td><td></td><td></td><td></td><td>3478</td><td>14.26</td><td>&lt; 0.0001</td></tr><tr><td rowspan="2">Other</td><td>Soil type</td><td></td><td></td><td></td><td>6443</td><td>6.61</td><td>&lt; 0.0001</td></tr><tr><td>Soil texture</td><td></td><td></td><td></td><td>x</td><td>x</td><td>x</td></tr><tr><td></td><td>Main crop group</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>Type of Study</td><td>1,52</td><td>5.16</td><td>0.0273</td><td></td><td></td><td></td></tr></tbody></table>

<footnote>* CC = cover crop; x = factor not tested</footnote>
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Change in yield (%) in intercropping compared to monocropping (a) and the modulating influence of cropping practices and edapho-climatic conditions (b-g). In (a) the violin plot represents the data distribution, and the red point and bar represent the mean and 95 % confidence interval predicted by the linear model from Table 3. The number of observations (n) is indicated. In (b,e) black lines represent the fitted fixed effect response, with associated p values. In (c,d,f,g) predicted means and confidence intervals are represented with ‘a’, ‘b’, and ‘c’ letters indicating significant differences in the pairwise comparison.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Change in yield (%) in crop rotation compared to monocropping (a) and the modulating influence of cropping practices and soil and climate conditions (b-g). In (a) the violin plot represents the data distribution, and the red point and bar represent the mean and 95 % confidence interval predicted by the linear model from Table 3. The number of observations (n) is indicated. In (d) black line represents the fitted fixed effect response, with associated p value. In (b,c,e,f,g) predicted means and confidence intervals are represented with ‘a’ and ‘b’ letters indicating significant differences in the pairwise comparison.</figcaption></figure>
### 3.1.1. Intercropping

Overall, the association of annual and cover crops resulted in an average yield increase of the annual crop of approximately + 7 % (Fig. 2a). However, the effects observed were highly variable, ranging from -50 % to + 240 %. This variability was partly attributed to certain edapho-climatic variables (Table 3). The positive effect of cover crops on annual crop yield was highly significant and positively correlated with altitude (p < 0.001; Fig. 2b), with greater yield gains observed in higher-altitude environments. Similarly, a significant relationship was observed between the positive effect of cover crops and the average temperature at the study site (p = 0.0166; Fig. 2e), with warmer environments being associated with greater yield gains. In semi-arid and oceanic zones, average yields decreased with cover crops by approximately 18 % and 10 %, respectively. In contrast, tropical and subtropical climates showed modest yield increases of around 1 % and 2 % (Fig. 2c). Soil type also influenced yield responses, with the highest positive yield ratio observed in Andosols (Fig. 2f). Management practices also influenced this variability. Simultaneous sowing of cover crops with annual crops resulted on average in a 18 % yield decrease of the main crop, whereas sowing before or after annual crops had virtually no noticeable effect on yield (Fig. 2d). Multi-family mixtures and species belonging to the Poaceae were associated with yield reductions of around -35 % and -5 %, respectively. Conversely, intercropping species from the Asteraceae and Fabaceae families contributed to higher average yield increases, estimated at around + 10 % and + 20 %, respectively (Fig. 2g).

### 3.1.2. Crop rotation

Crop rotation significantly improved the yield of annual crops, compared to CT, with an average gain of around + 25 %, despite high variability in responses (-50 % to +320 %, Fig. 3a). This effect depended on edapho-climatic conditions, as well as cropping practices (Table 3). The type of climate had a significant influence on the observed performance. Cropping systems located in oceanic climates showed an average gain of approximately + 35 %, compared with + 25 % in

tropical climates and + 10 % in subtropical climates, with these differences being statistically significant (Fig. 3b). The management of biomass from cover crops influenced this effect (p = 0.049). When this biomass was exported, the average yield ratio was limited to + 18 %. In contrast, it increased by + 32 % when left on the soil and by + 33 % when incorporated (Fig. 3c). Altitude also modulated the effects of cover crops grown in rotation with annual crops. A positive and significant correlation was observed between altitude (p = 0.0234; Fig. 3d) and yield ratio, indicating higher gains in high-altitude environments. Rainfall patterns had a strong influence on the observed performance. Rotational systems established in rainfall bimodal climates showed an average gain of around + 28 %. In comparison, those in unimodal rainfall climates showed an increase of around + 18 %, with this difference being statistically significant (Fig. 3b). The application of nitrogen fertilizer to the annual crop also influenced the magnitude of the response. Crops that received nitrogen fertilizer had an average gain of around + 26 % with the rotation, compared with + 18 % in the absence of fertilizer (Fig. 3f). Finally, the experimental context influenced the intensity of the effect. Trials conducted on experimental station yielded an average gain of + 30 %, whereas trials under farm conditions resulted in a more moderate increase of + 19 % (Fig. 3g).

### 3.2. Annual crop species response to cover crops

The effect of cover crops on annual crop yields differed according to crop type and reference yield (without cover) in intercropping and rotation systems (Fig. 4). In rotations, a very significant trend was observed for maize (Zea mays, p < 0.001), with more pronounced yield gains when the yield without cover crops was less than 5000 kg ha⁻¹ (Fig. 4a). This effect gradually decreased as the yield increased, becoming negative when the control annual crop reached 10,000 kg ha⁻¹. However, no significant effect was observed for rice (Oryza sativa). Among legumes (Fig. 4b), a significant trend was also observed for soybeans (Glycine max, p = 0.01), where the beneficial effects of cover crops were more pronounced at low yields (<

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Variation in change in yield (%) in crop rotation (a-c) or intercropping (d-f) with annual crop yields in monocropping systems (grain yield without cover crop). Linear responses per species are indicated by different color lines. The number of observations (n) and p values are indicated.</figcaption></figure>
3000 kg ha⁻¹), tending to diminish or become neutral at higher yields. Cotton (*Gossypium sp.*) showed a highly significant decline (p < 0.001) of the cover crop yield ratio (Fig. 4c), with positive effects observed at low yields (< 800 kg ha⁻¹) that decreased as the yield increased to 1200 kg ha⁻¹.

In intercropping systems, a very significant trend was also observed for maize (*Zea mays*, p < 0.001) (Fig. 4d), with a slightly positive and constant effect, higher yield gains when the yield without cover crop was less than 5000 kg ha⁻¹, followed by a gradual decrease in this effect as the yield increased to about 10,000 kg ha⁻¹. Rice (*Oryza sativa*) showed a very significant negative effect (p < 0.001; n = 13), with lower yields when the yield without cover crop was less than 3000 kg ha⁻¹, and a gradual decrease in this effect as the yield increased. No significant relationship was observed for sorghum (*Sorghum bicolor*), barley (*Hordeum vulgare*), and pearl millet (*Pennisetum glaucum*). Among legumes (Fig. 4e), cowpea (*Vigna unguiculata*) showed no significant trend (n = 13), with yields lower than those observed without cover crops at all yield levels. Cotton (*Gossypium sp.*) showed no significant effect (n = 22), with a few cases of positive yield responses at low yield levels (Fig. 4f).

### 3.3. Yield response of annual crops to different cover crop traits

#### 3.3.1. Intercropping

The main cover crop traits studied in intercropping systems were, in order of importance (Fig. 5a): aboveground dry mass (~28 %), grain yield (~26 %), plant height (~6 %), aboveground N content (~4 %), leaf area index (~3 %), tiller density (~2 %), individual grain weight (~2 %), number of pods per plant (~1.5 %), ground cover (~1 %), and leaf blade stem ratio (~1 %). Other traits were less frequently studied, with a study frequency of less than ~1 %. However, Fig. 6a revealed that not all the most frequently measured traits were decisive at explaining yield variation. The significant trait with the highest marginal R² values was the aboveground N mass (R²N = 0.3), indicating a significant contribution to the yield variability observed in intercropping systems. The relationship between the aboveground N mass and change in yield was significant and positive (p < 0.01; Fig. 6b). However, although plant height explained only a negligible part of the total variance (R²m = 0.01), a significant but negative relationship (p = 0.0152; Fig. 6c) was observed between change in yield and plant height. No significant relationship was observed between change in yield and other traits.

#### 3.3.2. Crop rotation

The main cover crop traits studied in rotational systems were, in order of importance (Fig. 5b): aboveground biomass (~30 %), nitrogen

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Frequency (%) of cover crop trait observations among available trials in intercropping and rotation systems. Variables are described in Table 2. The number of observations (n) is also indicated.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Change in yield (LogYield) response to individual cover crop traits in intercropping systems. (a) represent the marginal R square (Rm2), number of observations (n), and p-values of each linear model. (b) and (c) represent significant responses of change in yield with cover crop aboveground N mass (b) and height (c). The red lines represent the fitted fixed effects response.</figcaption></figure>

concentration in aboveground biomass (~10 %), phosphorus concentration in aboveground biomass (~6 %), potassium concentration in aboveground biomass (~6 %), C/N ratio (~5 %), root diameter (~3 %), and root length density (~3 %). Other traits were studied less frequently, with a study frequency of less than ~3 %. However, analysis of the variance of the logarithm of yield (Fig. 7a), revealed that the cover crop traits with the strongest explanatory power for crop yield in rotation were root diameter (R²m = 0.36) and root length density (R²m = 0.40). In contrast, aboveground dry mass explained a significantly smaller proportion of the variance in yield. Change in yield increased with cover crop root diameter and root length density (Fig. 7c and d). Change in yield also increased with cover crop aboveground biomass (p < 0.01), although its explanatory power remained low (R²m = 0.03). No significant relationship was observed between yield and the other traits.

## 4. Discussion

Our results showed that cover crops contribute to improving annual crop yields overall, both in rotation systems (+25 %) and in intercropping systems (+7 %). However, the extent of this effect varies considerably from one study to another. In the literature, this improvement was explained by several benefits attributed to cover crops: reduction in soil bulk density, improvement in soil structure and hydraulic properties, increased water infiltration and storage, better nutrient availability, and stimulation of microbial activity (Blanco-Canqui and Ruis, 2020; Koudahe et al., 2022; Garba et al., 2022). These mechanisms create a more favorable environment for the growth of annual crops. Our results are consistent with previous observations, which report average yield increases of between 2.6 % and 16 % for cereal and legume crops rotated with cover crops (Abdalla et al., 2019; Peng et al., 2024; He et al., 2025). Unlike these studies, which combine global data, our analysis focuses on tropical and subtropical areas. In this context, the

observed 25 % increase is significantly higher, suggesting that the effects of cover crops may be amplified in hot and humid environments, which are often characterized by low soil fertility and limited water availability (Garba et al., 2022). In such environments, cover crops rapidly restore lost soil organic matter (Rosolem et al., 2016; Laub et al., 2023) and substantially reduce erosion from intense rainfall (Muoni et al., 2019; Atakoun et al., 2023). Similarly, in intercropping systems, cover crops have shown their ability to improve yields, particularly for legumes, although the precise effect has not been quantified (Osipitan et al., 2018). The extent of the benefits may depend on the type of system. In rotation, cover crops are planted before the annual crop, thereby limiting competition for resources and maximizing their beneficial effects on the soil. In intercropping, coexistence with annual crops can lead to some competition for light, water, and nutrients, partially reducing these benefits. Despite this modest gain, intercropping with cover crops remains a good strategy for enhancing the productivity of annual crops. In temperate climates, cover crops provide primary benefits during fallow periods, particularly in winter. By maintaining ground cover, they protect soils against freezing, limit erosion from winter rainfall, and reduce nitrate leaching (Blanco-Canqui et al., 2015). In tropical and subtropical regions, dominated by small farms (Samberg et al., 2016) which are characterized by poor soils and unstable yields (Harvey et al., 2014; Dossouhoui et al., 2023), the integration of cover crops could therefore be an effective lever for enhancing productivity and improving the resilience of agricultural systems to climate change by stabilizing yields in these regions that are heavily dependent on rainfall (Amadé, 2025). The heterogeneity of results between the studies included in our meta-analysis was expected, given the diversity of edapho-climatic conditions and management practices.

### 4.1. Influence of climate and soil type

The yield effects of both intercropping and rotation with cover crops
<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Change in yield (LogYield) response to individual cover crop traits in crop rotation systems. (a) represent the marginal R square (Rm2), number of observations (n), and p-values of each linear model. (b,c,d) represent significant responses of change in yield with cover crop aboveground dry mass (b), root diameter (c) and root length density (d). The red lines represent the fitted fixed effects response.</figcaption></figure>

exhibit similar trends with respect to altitude and, to a lesser extent, climate, but they differ in their sensitivity to soil types, temperature, and rainfall patterns. In intercropping systems, the effect of cover crops on yield increased with altitude and temperature, becoming positive in tropical and subtropical climates but remaining negative in semi-arid and oceanic climates. Yields were also higher on Andosols than on other soil types. In rotation systems, the effect similarly increased with altitude and remained positive across oceanic, tropical, and subtropical climates, with bimodal rainfall patterns associated with greater yield gains compared to unimodal regimes. The positive relationship between altitude and the effect of cover crops in both systems may be linked to improvements in soil properties along altitudinal gradients. Linear increases in soil organic carbon and nitrogen, microbial biomass, litter accumulation, fine root density, and the soil C:N ratio with rising elevation, factors conducive to improve crop growth, have been reported (He et al., 2016). Tropical and subtropical climates also favored the expression of cover crop benefits, possibly due to greater climatic stability. It was found that interannual yield variability was lower in these regions, attributed to more consistent temperatures and fewer extreme events, thereby supporting more stable crop performance (Gleiser et al., 2021). In contrast, the negative effects observed in intercropping systems under semi-arid and oceanic climates may stem from heightened competition for limited resources under water-stressed conditions. It has been emphasized that aridity limits nutrient availability and slows crop development (Naorem et al., 2023), while the vulnerability of crops under declining soil moisture has also been highlighted (Dietz et al., 2021). These constraints are particularly critical in intercropping systems, where cover and annual crops simultaneously compete for resources.

The increasing positive effect of temperature (as well as difference

between tropical and subtropical climate conditions) on intercropping systems may be due to the faster decomposition of cover crop residues, which enhances nitrogen mineralization and nutrient availability. This mechanism is supported by observations that sugarcane yield responses to intercropped cover crops increased with temperature (Viaud et al., 2023). The higher performance observed on Andosols could be attributed to their favorable physicochemical properties, including high organic matter content, the presence of short-range-ordered minerals in close association, and a high capacity to retain water and nutrients (Kögel-Knabner and Amelung, 2014). These traits improve plant resilience under environmental stress. In contrast, other soil types often present structural or chemical limitations such as compaction, acidity, or low cation exchange capacity that may constrain the effectiveness of cover crops.

In rotation systems, bimodal rainfall regimes appeared to enhance the agronomic potential of cover crops by providing a more even distribution of water throughout the season. It was demonstrated that in cereal-based systems in Ethiopia, bimodal rainfall was associated with higher yields, while unimodal regimes with shorter, erratic rainy seasons limited crop performance (Wakjira et al., 2021). More broadly, in sub-Saharan Africa, the bimodal rainfall pattern enables the implementation of multi-seasonal cropping systems, which combine short rainy seasons, long rainy seasons, perennial, and fruit crops on the same plot within a single year (Tibesigwa et al., 2021). This configuration is particularly favorable to rotation systems. In such contexts, cover crops can be sown after the harvest of one annual crop and before the establishment of the next, allowing them to grow fully and deliver their full agronomic potential. The observed similarities between systems may be driven by overarching environmental factors such as altitude and climate type that exert a consistent influence on yield regardless of these
systems (Brouwer and Heibloem, 1986; Rabia et al., 2022). In contrast, the differences likely reflect the intrinsic logic of each system. Temporal succession in rotations may enable better exploitation of rainfall patterns, particularly in bimodal regimes; while the simultaneous coexistence of species in intercropping may amplify sensitivity to edapho-climatic constraints, such as temperature and soil type (Liu et al., 2025).

## 4.2. Influence of crop management practices

The management practices influencing cover crop effects varied with the cropping system. Simultaneous sowing of cover crops tended to reduce yields in intercropping systems, unlike delayed sowing. Fabaceae and Asteraceae showed positive effects, whereas Poaceae and mixtures of families had negative impacts in these systems. In rotation systems, yield gains were enhanced when biomass was retained, either left on the soil surface or incorporated, under experimental conditions and with nitrogen fertilization.

Yield reductions observed with simultaneous sowing often result from competition for water and nutrients between cover crops and annual crops, especially in resource-limited environments such as semiarid regions (Feng et al., 2024). This competition is particularly intense in low-input or organic systems, where annual crops are less competitive for resources like seedbed moisture. Delayed sowing, either before or after the annual crop, reduces this competition and allows better crop establishment (Feng et al., 2024). When cover crops are sown prior to the annual crop, perennial species such as Desmodium may provide long-term benefits despite short-term yield penalties. The contrasting effects among botanical families can be attributed to their distinct functional traits. Fabaceae improve soil fertility through biological nitrogen fixation (Hasanuzzaman et al., 2020), while Asteraceae exert allelopathic effects on weeds, including herbicide-resistant species, thereby reducing competition (Lopes et al., 2022). Conversely, Poaceae tend to be highly competitive and do not contribute to soil nitrogen enrichment, which limits their ability to benefit annual crops and may thus reduce their potential advantages. Although mixtures of families are valued for complementarity and functional diversity, they do not always guarantee high biomass production, especially when dominated by grasses, which can reduce the positive impact of cover crops (Finney et al., 2016).

In rotation systems, retaining biomass on-site by crop residue incorporation or mulching promotes water conservation and gradual nutrient release, contrasting with the negative effects of residue burning, which diminishes nutrient availability (Garba et al., 2022). Moreover, experimental trials often benefit from stricter agronomic controls, which partly explain the higher yield gains observed compared to smallholder farming conditions. The combined application of mineral nitrogen and cover crops optimizes soil fertility by improving nitrogen recovery, increasing organic matter and phosphorus availability, and limiting acidification, thereby maximizing annual crop yields (Hua et al., 2020). Differences between intercropping and rotation systems can mainly be attributed to the timing and nature of interactions between cover crops and annual crops. In intercropping, simultaneous growth necessitates careful species selection and sowing timing to minimize direct competition between species (Brooker et al., 2015; Shekhar et al., 2024). In rotation, cover crops are grown outside the annual cropping cycle, reducing immediate competition with the annual crop (Tanveer et al., 2019; Shekhar et al., 2024). Their effects depend more on post-harvest practices, such as residue management and fertilization, which influence resource availability for subsequent crops (Garba et al., 2022).

## 4.3. From facilitation to competition

In both cropping systems studied, cover crops had more frequent and stronger positive effects on annual crop yields in rotation, especially under low yields without cover crops. Conversely, effects in

intercropping systems were more variable, often limited, and sometimes negative. In rotation, cover crops significantly increased yields of corn and soybeans when yields without cover crops were low, as well as cotton yields below 800 kg ha⁻¹. No significant effect was observed on rice yields. In intercropping, positive effects were noted on maize below 5000 kg ha⁻¹, but rice exhibited negative effects below 3000 kg ha⁻¹; these effects generally diminished at higher yields without cover crops. No significant effects were detected for cotton, sorghum, barley, and millet under intercropping, while cowpeas showed a negative effect.

The observed yield improvements in rotation for corn, soybeans, and cotton under low yields without cover crops can be attributed to several agronomic benefits of cover crops, including organic matter enrichment, maintenance of carbon balance, enhancement of soil physical, chemical, and biological properties, nutrient recycling, temperature regulation, weed suppression, and erosion control (Adetunji et al., 2020; Koudahe et al., 2022; Atakoun et al., 2023; Yousefi et al., 2024). These findings align with previous studies, which reported yield increases of 30–33 % for corn, particularly when legumes or cover crop mixtures were used in rotation (Marcillo and Miguez, 2017). For cotton, yield gains may also be linked to a 20 % reduction in weed biomass (Toler et al., 2019), with more pronounced effects observed under unfavorable conditions (Zhao et al., 2022). In the case of hydromorphic conditions, as found in irrigated rice cultivation systems, the absence of effect on rice yield could be explained by slowed biomass decomposition, reduced mineralization and nutrient availability, and increased methane emissions without corresponding yield benefits (Shan et al., 2008).

In intercropping systems, effects were more heterogeneous. Maize benefited from positive effects at low yields without cover crops, consistent with findings in Benin (Yemadje et al., 2025a, 2025b). Conversely, rice showed negative effects, likely due to strong competition for resources with cover crops, which could also explain the negative effects on cowpeas, even at low yields. A sharp decline in soybean populations with increasing cover crop biomass has been demonstrated, highlighting competitive pressures (Liebert et al., 2017). Although cowpeas employ distinct ecological strategies, dense cover crops can still have an adverse effect on them. The absence of significant effects on cotton, sorghum, millet, or barley may reflect either a lower responsiveness to environmental changes induced by cover crops or insufficient data and high contextual variability (Cumming, 2013).

Overall, these results indicate that, regardless of cropping system, the benefits of cover crops are more pronounced in low yielding environments, where they improve soil fertility, structure, and nutrient availability, and limit weed pressure. This facilitative effect tends to diminish or reverse into competition as yields without cover crops increase and resources become limiting for the annual crop.

## 4.4. Are cover crop traits useful to explain yield effects?

Our meta-analysis reveals that studies on cover crop traits predominantly focus on the aboveground parts in both rotation and intercropping systems, while their links to crop yield remain inconclusive. In rotation, the traits most often measured relate to biomass and nutrient content, with relatively little attention paid to architecture and root development. In intercropping, the traits evaluated primarily focus on the aboveground parts (architecture, biomass, nutrients, and yield parameters), while root traits remain largely understudied.

The predominance of aboveground traits in both systems reflects a general trend in the literature, where understanding the relationships between traits and agroecological performance relies largely on studying the aboveground parts of plants. Belowground traits, particularly those related to root absorption, remain understudied despite their important role in essential plant functions (Da et al., 2023). This focus on aboveground traits may be explained in part by their accessibility and relative ease of measurement compared to root traits, which are more complex to assess.

In rotation, aboveground dry biomass, its macronutrient
composition (N, P, K), and the C/N ratio were the most studied traits, as they influence organic matter and nutrition for the next crop. The quantity and nutritional quality of cover crop biomass are crucial for enhancing yields and soil fertility, which highlights the need to adapt cover crop management, particularly the timing of termination, by using digital tools to optimize nutrient recycling (Bai et al., 2024). However, in these rotation systems, beyond biomass, the root traits, particularly root diameter and density of cover crops, were good predictors of yield variations. These results confirm the importance of the services provided by underground roots, such as improving soil structure, interacting with the rhizosphere, and acquiring resources (Griffiths et al., 2022), and highlight the limited predictive power of above-ground characteristics regarding the effects of cover crops on annual crop yields.

However, other potentially decisive traits remain understudied in these systems. This is the case for total dry biomass (aerial and root), which are rarely measured despite their promising effects on soil fertility, or leaf area index, and its relation to crop-weed competition. Legumes such as pigeon peas, mucuna, and cowpeas have been shown to enrich the soil with nitrogen and enhance the performance of rice grown in rotation (de Borja Reis et al., 2017). Mucuna, in particular, has a high leaf index and rapid ground cover, enabling it to compete effectively with Imperata, a weed that limits maize yields. In addition, it has the nutritional composition of above-ground biomass, particularly in terms of calcium, magnesium, sulfur, and trace elements such as manganese, iron, zinc, boron, copper, and molybdenum, remains largely unexplored. However, some studies suggest that they influence yields. It has been demonstrated that cover crop rich in elements can compensate for low levels of calcium and magnesium in the soil, leading to a significant improvement in soybean yields (E Silva et al., 2024). Finally, certain root traits, such as the carbon-to-nitrogen ratio of the roots, could play a crucial role. The decomposition of Persian clover roots, characterized by a low C/N ratio, has been shown to enrich the soil with nitrogen and contribute to increased yields in subsequent crops, although the direct effect on rice was not evaluated (Weinert et al., 2023).

In intercropping systems, the traits studied mainly concern productivity, growth vigor, and competitiveness between species. Above-ground dry biomass and height reflect the ability of cover crops to capture light, occupy space, and compete with annual crops (Yu et al., 2015; Lithourgidis et al., 2011). The aboveground N mass, particularly in legumes, indicates their ability to fixate nitrogen and contribute to their nutritional value (Fustec et al., 2010). Leaf area index (LAI) influences light capture and the microclimate of the canopy (Parker, 2020). The leaf-to-stem ratio provides information on the quality of residues, particularly in forage grasses (Zailan et al., 2018). Reproductive traits, such as grain weight, pod number, or grain yield, are used to evaluate overall productivity. Finally, plant cover traits provide indicator of soil erosion protection and controls weeds (Blanco-Canqui et al., 2015). However, only two traits contribute significantly to the variation in annual crop yields in our study: height and above-ground nitrogen mass. The height of cover crops, although not very explanatory, was negatively correlated with annual crop yields, suggesting competition through shading (MacLaren et al., 2023). Regarding aboveground nitrogen mass (aboveground N mass), the higher it is, the more positive the effect on annual crop yield tends to be, suggesting better nitrogen recovery in the system and increased nutrient supply for the annual crop.

Certain traits, which are less frequently measured, could nevertheless play an important role in interspecific interactions. For example, the total dry biomass produced by certain grasses (Rodrigues et al., 2022) have not been linked to annual crop yields, despite their potential relevance in improving soil structure and reducing erosion. Similarly, several studies have reported variations in the mineral content of cover crops (Mg, Ca, K, P, Zn, Fe, Mn, Cu, B) in intercropping systems with legumes (Nasar et al., 2024; Dimande et al., 2024), which can facilitate the supply of essential nutrients for annual crops, while their direct impact on yields remains to be analyzed. The C/N ratio of biomass,

which influences the rate of decomposition and nitrogen release (Coser et al., 2015), as well as the leaf/stem ratio in certain grasses (Santos et al., 2020), remain under-exploited in terms of their link to annual crop yields. Other traits such as the number of living leaves per tiller, leaf blade length, senescence or leaf elongation rate, phylochrone (Rodrigues et al., 2022), and branch production (Emuh, 2007) have been studied without a direct link to yield being demonstrated, despite their potential role regarding light competition. In addition, traits related to the root system have also been little explored. For example, root N content (Raji and Dörsch, 2020) and the deep rooting of faba beans in association (Agegnehu et al., 2008) have been described without analyzing their effect on yield. However, these root traits have the ability to promote the acquisition of water and nutrient resources for annual crops, thereby improving their productivity, or, conversely, to increase interspecific competition, which could limit yields. Although nodulation is essential in legumes, it is rarely considered in studies for its effect on annual crop yields. However, it has been shown that in cowpeas, neither the number nor the weight of nodules had a significant effect on intercrop yields (Namatsheve et al., 2021).

Our meta-analysis reveals a significant discrepancy between the most frequently measured traits and those that actually explain yield variability. This discrepancy between statistical significance and explanatory power can be explained in part by the high heterogeneity of contexts (climate, altitude, temperature, biomass management, fertilization, etc.), already identified in our meta-analysis as a major factor modulating the effects of cover crops in both systems. Only certain traits, particularly those related to the root system and nitrogen nutrition, contribute significantly to the variation in annual crop yields. The most common traits explain yield variations only weakly and do not show a significant linear relationship, which calls into question the practical relevance of the time and effort devoted to measuring them.

### 4.5. Limitations of the study, recommendations and way forward

The effects of cover crops on annual crop yields are strongly influenced by the heterogeneity of agricultural contexts. This variability arises from factors such as cropping systems, cultural practices, edaphoclimatic conditions, and the traits of cover crop species. These parameters shape the interactions between annual and cover crops, affecting the magnitude of yield gains or losses. To optimize cover crop use in diverse conditions, predictive models that integrate these parameters are needed. Such models should combine climatic and soil data, local farming practices, and cover crop traits to predict their impacts on annual crop yields. This approach would enhance the accuracy of agronomic predictions and facilitate the development of performance maps for various agroecological zones. These maps would guide farmers and decision-makers in selecting appropriate species and practices for their contexts. Implementing these models requires systematic and detailed data collection on all relevant factors, including belowground traits, which are often overlooked in current research.

## 5. Conclusions

Cover crops are grown for a variety of reasons, such as maintaining soil fertility in the long term, enhancing the presence of natural enemies of pests, suppressing weeds, reducing soil erosion, etc., but such benefits may come at a yield cost when cover crops compete with the main crop in space and time. This meta-analysis highlights that next to such services, cover crops may also significantly increase annual crop yields in tropical and subtropical environments, with average gains of + 25 % in crop rotations and + 7 % in intercrops. These effects varied greatly depending on cultural practices and edapho-climatic conditions. In rotation, the beneficial effects were amplified by the incorporation of residues into the soil, nitrogen input, and in bimodal and tropical climates, as well as at different altitudes. In intercropping, gains were further enhanced in Andosols, by the use of *Fabaceae* as cover crops,
staggered sowing (before or after the main crop), as well as altitude and temperature, and in subtropical climates. The effect of cover crops also varied according to the type of annual crop. In rotation, they significantly improved the yields of maize, soybeans, and cotton, particularly when yields without cover crops were low, with a decreasing effect as these yields increased. In intercropping, only maize showed a positive effect at low yields without cover crops, while rice showed a negative effect under the same conditions. Analysis of cover crop traits revealed that in rotation systems, root diameter and root density explained part of the yield variability and were positively associated with increase in yield. In intercropping, only individual grain weight and aboveground nitrogen content were explanatory. Aboveground dry mass was also positively associated with change in yield in rotation, while aboveground nitrogen content and height were positively and negatively correlated in intercropping. Therefore, the choice of cover crop species must be adapted to the cropping system, cultural practices, edaphoclimatic conditions, and the specific traits of the cover crops. Fabaceae cover crops tend to perform better in intercropping, particularly in subtropical climates and in Andosols, where they can enhance the productivity of annual crops. In tropical rotation systems, the conservation of residues from cover crops helps improve yields; however, this effect is even greater when nitrogen fertilizer is added. Cover crops should be a priority in contexts of low initial yields, whether in rotation or in intercropping, as they help mitigate productivity constraints of small-holder farmers. Furthermore, while certain traits partially explain the variability in yield responses, their predictive power remains limited due to a lack of evidence, which calls for further research to refine recommendations. So far, the relationship of cover crop traits with cropping system productivity is not conclusive enough as to guide management decisions, at least in tropical and subtropical regions.

## CRediT authorship contribution statement

**Mathias Christina:** Writing – review & editing, Visualization, Validation, Methodology, Formal analysis, Data curation. **Pierrot Lional Yemadje:** Writing – review & editing, Validation, Methodology, Conceptualization. **Benjamin Heuclin:** Writing – review & editing, Visualization, Methodology, Formal analysis, Data curation. **Hervé S. Nonwègnon Aholoukpè:** Writing – review & editing, Investigation, Conceptualization. **Pablo Tittonell:** Writing – review & editing, Investigation, Conceptualization. **Hermione Koussihouèdé:** Writing – review & editing, Validation, Methodology, Conceptualization. **Fassinou Demayi Jorès Mauryo:** Writing – review & editing, Methodology, Formal analysis, Data curation, Conceptualization.

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

We would like to thank the Cooperation and Cultural Action Department of the French Embassy in Benin for awarding a scholarship for our research work. We also thank the Benin Cotton Research Institute (IRC), Cotton Interprofessional Association (AIC), and the TAZCO2 project (Transition Agroécologique des Zones Cotonnières du Bénin-CBJ1255 01 G), which is funded by the Republic of Benin and the French Development Agency (AFD).

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.fcr.2025.110201.

## Data availability

Data will be made available on request. The R script used for statistical analysis and figures are available on Zenodo (Christina et al., 2025).

## References

Abdalla, M., Hastings, A., Cheng, K., Yue, Q., Chadwick, D., Espenberg, M., Truu, J., Rees, R.M., Smith, P., 2019. A critical review of the impacts of cover crops on nitrogen leaching, net greenhouse gas balance and crop productivity. Glob. Change Biol. 25, 2530–2543. https://doi.org/10.1111/gcb.14644.

Acharya, B.S., Dodla, S., Gaston, L.A., Darapuneni, M., Wang, J.J., Sepat, S., Bohara, H., 2019. Winter cover crops effect on soil moisture and soybean growth and yield under different tillage systems. Soil Tillage Res. 195, 104430. https://doi.org/10.1016/j.still.2019.104430.

Adetunji, A.T., Ncube, B., Mulidzi, R., Lewu, F.B., 2020. Management impact and benefit of cover crops on soil quality: a review. Soil Tillage Res. 204, 104717. https://doi.org/10.1016/j.still.2020.104717.

Aagegnehu, G., Ghizaw, A., Sinebo, W., 2008. Yield potential and land-use efficiency of wheat and faba bean mixed intercropping. Agron. Sustain. Dev. 28, 257–263. https://doi.org/10.1051/agro:2008012.

Akaike, H., 1998. Information theory and an extension of the maximum likelihood principle. In: Parzen, E., Tanabe, K., Kitagawa, G. (Eds.), Selected Papers of Hirotugu Akaike, Springer Series in Statistics. Springer New York, New York, NY, pp. 199–213. https://doi.org/10.1007/978-1-4612-1694-0-15.

Allakonon, M.G.B., Zakari, S., Tovihoudji, P.G., Fatondji, A.S., Akponikpè, P.I., 2022. Grain yield, actual evapotranspiration and water productivity responses of maize crop to deficit irrigation: a global meta-analysis. Agric. Water Manag. 270, 107746. https://doi.org/10.1016/j.agwat.2022.107746.

Altieri, M.A., Nicholls, C.I., 2020. Agroecology: challenges and opportunities for farming in the anthropocene. Cienc. E Investig. Agrar. Rev. Latinoam. Cienc. Agric. 47, 204–215. https://doi.org/10.7764/ijanr.v47i3.2281.

Amadé, N., 2025. Effet du changement climatique sur le rendement du mil, du sorgho et du maïs au sahel: un modèle ARDL –818 Afr. Sci. J. 3, 818. https://doi.org/10.5281/zenodo.14957127.

Atakoun, A.M., Tovihoudji, P.G., Diogo, R.V., Yemadje, P.L., Balarabe, O., Akponikpè, P. I., Sekloka, E., Hougni, A., Tittonell, P., 2023. Evaluation of cover crop contributions to conservation agriculture in Northern Benin. Field Crops Res. 303, 109118. https://doi.org/10.1016/j.fcr.2023.109118.

Bai, G., Koehler-Cole, K., Scoby, D., Thapa, V.R., Basche, A., Ge, Y., 2024. Enhancing estimation of cover crop biomass using field-based high-throughput phenotyping and machine learning models. Front. Plant Sci. 14, 1277672. https://doi.org/10.3389/fpls.2023.1277672.

Barbieri, P., Starck, T., Voisin, A.-S., Nesme, T., 2023. Biological nitrogen fixation of legumes crops under organic farming as driven by cropping management: a review. Agric. Syst. 205, 103579. https://doi.org/10.1016/j.agsy.2022.103579.

Begg, C.B., Mazumdar, M., 1994. Operating characteristics of a rank correlation test for publication bias. Biometrics 50, 1088–1101. https://doi.org/10.2307/2533446.

Blanco-Canqui, H., Ruis, S.J., 2020. Cover crop impacts on soil physical properties: a review. Soil Sci. Soc. Am. J. 84, 1527–1576. https://doi.org/10.1002/saj2.20129.

Blanco-Canqui, H., Shaver, T.M., Lindquist, J.L., Shapiro, C.A., Elmore, R.W., Francis, C. A., Hergert, G.W., 2015. Cover crops and ecosystem services: insights from studies in temperate soils. Agron. J. 107, 2449–2474. https://doi.org/10.2134/agronj15.0086.

Borges, L.P., da Silva, A.G., de Andrade, C.L.L., Goulart, M.M.P., Teixeira, I.R., de Pinho Costa, K.A., 2022. Intercropping of sorghum with Congo grass (brachiaria ruziziensis) on soybean succession at the rainy season. -i Aust. J. Crop Sci. 16, 997. https://doi.org/10.21475/ajcs.22.16.08.p2078.

de Borja Reis, A.F., de Almeida, R.E.M., Junior, A.F.C., Nascente, A.S., 2017. Plantas de cobertura afetando atributos de solo, nutrição e produtividade do arroz irrigado tropical. Rev. Caatinga 30, 837–846. https://doi.org/10.1590/1983-21252017v30n40rc3.

Brooker, R.W., Bennett, A.E., Cong, W., Daniell, T.J., George, T.S., Hallett, P.D., Hawes, C., Iannetta, P.P.M., Jones, H.G., Karley, A.J., Li, L., McKenzie, B.M., Pakeman, R.J., Paterson, E., Schöb, C., Shen, J., Squire, G., Watson, C.A., Zhang, C., Zhang, F., Zhang, J., White, P.J., 2015. Improving intercropping: a synthesis of research in agronomy, plant physiology and ecology. N. Phytol. 206, 107–117. https://doi.org/10.1111/nph.13132.

Brouwer, C., Heibloem, M., 1986. Irrigation water management: irrigation water needs. Train. Man. 3, 1–5.

Chikoye, D., Ekeleme, F., 2003. Cover crops for cogongrass (Imperata cylindrica) management and effects on subsequent corn yield. Weed Sci. 51, 792–797. https://doi.org/10.1614/P2002-095.

Christina, M., Fassinou, J.M., Benjamin, H., 2025. Script for journal article " influence of cover crops and their traits on the yield of main annual crops grown in tropical and subtropical environments - a meta-analysis. Zenodo. https://doi.org/10.5281/zenodo.15698011.

Coser, T.R., Ramos, M.L.G., de Figueiredo, C.C., Urquiaga, S., de Carvalho, A.M., Barros, F.V., Mendonça, M.T., 2015. Nitrogen uptake efficiency of maize in monoculture and intercropped with brachiaria humidicola and panicum maximum in a dystrophic Red-Yellow latosol of the Brazilian cerrado. Crop Pasture Sci. 67, 47–54. https://doi.org/10.1071/CP15077.

Cumming, G., 2013. Understanding the New Statistics: Effect Sizes, Confidence Intervals, And Meta-analysis. Routledge. https://doi.org/10.4324/9780203807002.
Da, R., Fan, C., Zhang, C., Zhao, X., Von Gadow, K., 2023. Are absorptive root traits good predictors of ecosystem functioning? A test in a natural temperate forest. N. Phytol. 239, 75–86. https://doi.org/10.1111/nph.18915.

Damour, G., Dorel, M., Quoc, H.T., Meynard, C., Risède, J.-M., 2014. A trait-based characterization of cover plants to assess their potential to provide a set of ecological services in banana cropping systems. Eur. J. Agron. 52, 218–228. https://doi.org/10.1016/j.eja.2013.09.004.

Daryanto, S., Jacinthe, P.-A., Fu, B., Zhao, W., Wang, L., 2019. Valuing the ecosystem services of cover crops: barriers and pathways forward. Agric. Ecosyst. Environ. 270, 76–78. https://doi.org/10.1016/j.agee.2018.10.021.

Delgado, J.A., Mosquera, V.H.B., Alwang, J.R., Villacis-Aveiga, A., Ayala, Y.E.C., Neer, D., Monar, C., López, L.O.E., 2021. Potential use of cover crops for soil and water conservation, nutrient management, and climate change adaptation across the tropics. Adv. Agron. 165, 175–247. https://doi.org/10.1016/bs.agron.2020.09.003.

Dietz, K.-J., Zörb, C., Geilfus, C.-M., 2021. Drought and crop yield. Plant Biol. 23, 881–893. https://doi.org/10.1111/plb.13304.

Dimande, P., Arrobas, M., Rodrigues, M.A., 2024. Intercropped maize and cowpea increased the land equivalent ratio and enhanced crop access to more nitrogen and phosphorus compared to cultivation as sole crops. Sustainability 16, 1440. https://doi.org/10.3390/su16041440.

Dossouhoui, G.A., Yemadje, P.L., Diogo, R.V.C., Balarabe, O., Tittonell, P., 2023. Sedentarisation" of tranşuhamant pastoralists results in privatization of resources and soil fertility decline in West Africa's cotton belt. Front. Sustain. Food Syst. 7, 1120315. https://doi.org/10.3389/fsufs.2023.1120315.

Duval, S., Tweedie, R., 2000. Trim and fill: a simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics 56, 455–463. https://doi.org/10.1111/j.0006-341X.2000.00455.x.

E Silva, J.A.G., De Pinho Costa, K.A., Da Costa Severiano, E., Da Silva, A.G., Vilela, L., Leandro, W.M., Muniz, M.P., Da Silva, L.M., Magalhães Mendonça, K.T., Barros, V. M., 2024. Efficiency of desiccation, decomposition and release of nutrients in the biomass of forage plants of the genus *brachiaria* after intercropping with sorghum in integrated systems for soybean productivity. Commun. Soil Sci. Plant Anal. 55, 1644–1662. https://doi.org/10.1080/00103624.2024.2323076.

Emuh, F.N., 2007. Economic yield and sustainability of maize crop (Zea mays (L.)) in association with cowpea (Vigna unguiculata (L.) Walp) and egusi-melon (Citrullus lunatus (Thumb) Mansf) in south western Nigeria. https://doi.org/10.3923/ja.2007.157161.

Eze, J.N., Ibrahim, P.A., Tiamiyu, S.A., Alfa, M., 2020. Assessment of drought occurrences and its implications on agriculture in Niger state, Nigeria. Discov. Agric. 6, 1–10.

Feng, X., Alletto, L., Cong, W.-F., Labreuche, J., Lamichhane, J.R., 2024. Strategies to improve field establishment of cover crops. A review. Agron. Sustain. Dev. 44, 47. https://doi.org/10.1007/s13593-024-00986-0.

Hasanuzzaman, M., Araújo, S., Gill, S.S. (Eds.), 2020. The Plant Family Fabaceae: Biology and Physiological Responses to Environmental Stresses. Springer Singapore, Singapore. https://doi.org/10.1007/978-981-15-4752-2.

Finney, D.M., White, C.M., Kaye, J., 2016. Biomass production and carbon/nitrogen ratio influence ecosystem services from cover crop mixtures. Agron. J. 108, 39–52. https://doi.org/10.2134/agronj15.0182.

Fustec, J., Lesuffleur, F., Mahieu, S., Cliquet, J.-B., 2010. Nitrogen rhizodeposition of legumes. A review. Agron. Sustain. Dev. 30, 57–66. https://doi.org/10.1051/agro/2009003.

Garba, I.I., Bell, L.W., Williams, A., 2022. Cover crop legacy impacts on soil water and nitrogen dynamics, and on subsequent cover crops in drylands: a meta-analysis. Agron. Sustain. Dev. 42, 34. https://doi.org/10.1007/s13593-022-00760-0.

Garnier, E., Navas, M.-L., 2012. A trait-based approach to comparative functional plant ecology: concepts, methods and applications for agroecology. A review. Agron. Sustain. Dev. 32, 365–399. https://doi.org/10.1007/s13593-011-00363-y.

Gleiser, G., Leme da Cunha, N., Sáez, A., Aizen, M.A., 2021. Ecological correlates of crop yield growth and interannual yield variation at a global scale. Web Ecol. 21, 15–43. https://doi.org/10.5194/we-21-15-2021.

Griffiths, M., Delory, B.M., Jawahir, V., Wong, K.M., Bagnall, G.C., Dowd, T.G., Nusinow, D.A., Miller, A.J., Topp, C.N., 2022. Optimisation of root traits to provide enhanced ecosystem services in agricultural systems: a focus on cover crops. Plant Cell Environ. 45, 751–770. https://doi.org/10.1111/pce.142427.

Harvey, C.A., Rakotobe, Z.L., Rao, N.S., Dave, R., Razafahlatratra, H., Rabarijohn, R.H., Rajaofara, H., MacKinnon, J.L., 2014. Extreme vulnerability of smallholder farmers to agricultural risks and climate change in Madagascar. Philos. Trans. R. Soc. B. Biol. Sci. 369, 20130089. https://doi.org/10.1098/rstb.2013.0089.

He, X., Hou, E., Liu, Y., Wen, D., 2016. Altitudinal patterns and controls of plant and soil nutrient concentrations and stoichiometry in subtropical China. Sci. Rep. 6, 24261. https://doi.org/10.1038/srep24261.

He, Q., Lu, C., Cowie, A., Zhao, S., Liu, D.L., Yi, B., Shi, L., Zhang, S., Qiu, T., Shi, Y., 2025. Optimizing cover cropping application for sustainable crop production. Npj Sustain. Agric. 3, 10. https://doi.org/10.1038/s44264-025-00050-8.

Hedges, L.V., Gurevitch, J., Curtis, P.S., 1999. The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150–1156. https://doi.org/10.1890/0012-9658 (1999)080 [1150:TMAORR]2.0.CO;2.

Hua, W., Luo, P., An, N., Cai, F., Zhang, S., Chen, K., Yang, J., Han, X., 2020. Manure application increased crop yields by promoting nitrogen use efficiency in the soils of 40-year soybean-maize rotation. Sci. Rep. 10, 14882. https://doi.org/10.1038/s41598-020-71932-9.

Jabran, K., Mahajan, G., Sardana, V., Chauhan, B.S., 2015. Allelopathy for weed control in agricultural systems. Crop Prot. 72, 57–65. https://doi.org/10.1016/j.cropro.2015.03.004.

Josse, J., Husson, F., 2016. missMDA: a package for handling missing values in multivariate data analysis. J. Stat. Softw. 70, 1–31. https://doi.org/10.18637/jss.v070i01.

Kinyua, M., Cao Diogo, R.V., Sibomana, J., Bolo, P.O., Gbedjissokpa, M.G., Mukiri, J., Muklama, J., Paul, B.K., Sommer, R., Kihara, J.M., 2019. Green manure cover crops in Benin and Western Kenya-A review.

Kögel-Knabner, I., Amelung, W., 2014. Dynamics, chemistry, and preservation of organic matter in soils. https://doi.org/10.1016/B978-0-08-095975-7.01012-3.

Kottek, M., Grieser, J., Beck, C., Rudolf, B., Rubel, F., 2006. World map of the Köppen-Geiger climate classification updated. https://doi.org/10.1127/0941-2948/2006/0130.

Koudahe, K., Allen, S.C., Djamian, K., 2022. Critical review of the impact of cover crops on soil properties. Int. Soil Water Conserv. Res. 10, 343–354. https://doi.org/10.1016/j.iswcr.2022.03.003.

Lajeunesse, M.J., 2011. On the meta-analysis of response ratios for studies with correlated and multi-group designs. Ecology 92, 2049–2055. https://doi.org/10.1890/11-0423.1.

Laub, M., Corbeels, M., Couédel, A., Ndungu, S.M., Mucheru-Muna, M.W., Mugendi, D., Necpalova, M., Waswa, W., Van de Broek, M., Vanlauwe, B., 2023. Managing soil organic carbon in tropical agroecosystems: evidence from four long-term experiments in Kenya. Soil 9, 301–323. https://doi.org/10.5194/soil-9-301-2023.

Lavorel, S., Garnier, E., 2002. Predicting changes in community composition and ecosystem functioning from plant traits: revisiting the holy grail. Funct. Ecol. 16, 545–556. https://doi.org/10.1046/j.1365-2435.2002.00664.x.

Lenth, R., Singmann, H., Love, J., Buerkner, P., Herve, M., 2018. emmeans: Estimated marginal means, aka least-Squares means. R package (version 1.3). https://CRAN.R-project.org/package=emmeans.

Liebert, J.A., DiTommaso, A., Ryan, M.R., 2017. Rolled mixtures of barley and cereal rye for weed suppression in cover crop-based organic no-till planted soybean. Weed Sci. 65, 426–439. https://doi.org/10.1017/wsc.2016.23.

Lithourgidis, A.S., Dordas, C.A., Damalas, C.A., Vlachostergios, D., 2011. Annual intercrops: an alternative pathway for sustainable agriculture, 0 Aust. J. Crop Sci. 5, 396–410. https://search.informit.org/doi/10.3316/informit.281409060336481.

Liu, H., Gao, X., Li, C., Cai, Y., Song, X., Zhao, X., 2025. Intercropping increases plant water availability and water use efficiency: a synthesis. Agric. Ecosyst. Environ. 379, 109360. https://doi.org/10.1016/j.agee.2024.109360.

Lobd, B.D., Di Tommaso, S., Zhou, Q., Ma, Y., Specht, J., Guan, K., 2025. The mixed effects of recent cover crop adoption on US cropland productivity. Nat. Sustain. 1–9. https://doi.org/10.1038/s41893-025-01599-5.

Lopes, A.D., Nunes, M.G.I.F., Francisco, J.P., dos Santos, E.H., 2022. Potential Allelopathic Effect of Species of the Asteraceae Family and Its Use in Agriculture. In: Vegetation dynamics, changing ecosystems and human responsibility. IntechOpen. https://doi.org/10.5772/intechopen.108709.

MacLaren, C., Waswa, W., Aliyu, K.T., Claessens, L., Mead, A., Schöb, C., Vanlauwe, B., Storkey, J., 2023. Predicting intercrop competition, facilitation, and productivity from simple functional traits. Field Crops Res. 297, 108926. https://doi.org/10.1016/j.fcr.2023.108926.

Marcillo, G.S., Miguez, F.E., 2017. Corn yield response to winter cover crops: an updated meta-analysis. J. Soil Water Conserv. 72, 226–239. https://doi.org/10.2489/jswc.72.3.226.

Martin, A.R., Isaac, M.E., 2015. REVIEW: plant functional traits in agroecosystems: a blueprint for research. J. Appl. Ecol. 52, 1425–1435. https://doi.org/10.1111/1365-2664.12526.

Martin, A.R., Isaac, M.E., 2018. Functional traits in agroecology: advancing description and prediction in agroecosystems. J. Appl. Ecol. 55, 5–11. https://doi.org/10.1111/1365-2664.13039.

Munir, S., Bashir, N.H., 2019. Crop diversity and pest management in sustainable agriculture. J. Integr. Agric. 18, 1945–1952. https://doi.org/10.1016/S2095-3119 (91)62689-4.

Muoni, T., Koomson, E., Òborn, I., Marohn, C., Watson, C.A., Bergkvist, G., Barnes, A., Cadisch, G., Duncan, A., 2019. Reducing soil erosion in smallholder farming systems in east Africa through the introduction of different crop types. Exp. Agric. 56, 183–195. https://doi.org/10.1017/S0014479719000280.

Namatsheva, T., Chikowo, R., Corbeels, M., Mouquet-Rivier, C., Icard-Vernière, C., Cardinael, R., 2021. Maize-cowpea intercropping as an ecological intensification option for low input systems in sub-humid Zimbabwe: productivity, biological N2-fixation and grain mineral content. Field Crops Res. 263, 108052. https://doi.org/10.1016/j.fcr.2020.108052.

Naorem, A., Jayaraman, S., Dang, Y.P., Dalal, R.C., Sinha, N.K., Rao, C.S., Patra, A.K., 2023. Soil constraints in an arid environment—challenges, prospects, and implications. Agronomy 13, 220. https://doi.org/10.3390/agronomy13010220.

NASA POWER 2025. Data Access Viewer (DAV) [WWW Document], n.d. URL https://po.wer.larc.nasa.gov/data-access-viewer/ (accessed 8.6.25).

Nasar, J., Ahmad, M., Gitari, H., Tang, L., Chen, Y., Zhou, X.-B., 2024. Maize/soybean intercropping increases nutrient uptake, crop yield and modifies soil physio-chemical characteristics and enzymatic activities in the subtropical humid region based in southwest China. BMC Plant Biol. 24, 434. https://doi.org/10.1186/s12870-024-05061-0.

Naudin, K., Husson, O., Scopel, E., Auzoux, S., Giner, S., Giller, K.E., 2015. PRACT (Prototyping Rotation and Association with Cover crop and no Till)-a tool for designing conservation agriculture systems. Eur. J. Agron. 69, 21–31. https://doi.org/10.1016/j.eja.2015.05.003.

Ortiz-Bobea, A., Ault, T.R., Carrillo, C.M., Chambers, R.G., Lobell, D.B., 2021. Anthropogenic climate change has slowed global agricultural productivity growth. Nat. Clim. Change 11, 306–312. https://doi.org/10.1038/s41558-021-01000-1.
Osipitan, O.A., Dille, J.A., Assefa, Y., Knezevic, S.Z., 2018. Cover crop for early season weed suppression in crops: systematic review and meta-analysis. Agron. J. 110, 2211–2221. https://doi.org/10.2134/agronj2017.12.0752.

Ouedraogo, Y., Taonda, J.S., Sermé, I., Tychon, B., Fielders, C.L., 2020. Factors driving cereal response to fertilizer microdosing in sub-Saharan Africa: a meta-analysis. Agron. J. 112, 2418–2431. https://doi.org/10.1002/agi2.20229.

Parker, G.G., 2020. Tamm review: leaf area index (LAI) is both a determinant and a consequence of important processes in vegetation canopies. For. Ecol. Manag. 477, 118496. https://doi.org/10.1016/j.foreco.2020.118496.

Peng, Y., Wang, L., Jacinthe, P.-A., Ren, W., 2024. Global synthesis of cover crop impacts on main crop yield. Field Crops Res. 310, 109343. https://doi.org/10.1016/j.fcr.2024.109343.

Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., 2021. R. Core team. 2021. nlme: linear and nonlinear mixed effects models. R. Package 3, 1–152. (https://CRAN.R-project.org/package=nlme) (version).

Pisarcik, M., Hakl, J., Toleikiene, M., Fuksa, P., Rasmussen, J., Hood-Nowotny, R., 2024. Role of cover crop roots in soil organic carbon accrual—a review. Eur. J. Soil Sci. 75, e13532. https://doi.org/10.1111/ejss.13532.

Rabia, A.H., Neupane, J., Lin, Z., Lewis, K., Cao, G., Guo, W., 2022. Principles and applications of topography in precision agriculture. Adv. Agron. 171, 143–189. https://doi.org/10.1016/bs.agron.2021.08.005.

Raji, S.G., Dörsch, P., 2020. Effect of legume intercropping on n 2 o emissions and CH 4 uptake during maize production in the great rift valley, Ethiopia. Biogeosciences 17, 345–359. https://doi.org/10.5194/bg-17-345-2020.

Rodrigues, R.C., Costa, C., dos, S., Teixeira, M.C., Miranda, B.E., de, C., Marinho, I.R., da Silva, E.C., Costa, K.A., de, P., de Souza, Y.L., Barros, D.M.A., de Araujo, I.G.R., 2022. Productive characteristics and economic analysis of tropical forage grasses cultivated under different sowing methods in intercropping production systems. Aust. J. Crop Sci. 16, 933–940. https://doi.org/10.21475/ajcs.22.16.07.p3603.

Rohatgi, A., 2020. WebPlotDigitizer (Version 4.8) [Computer software]. (https://auto meris.io/WebPlotDigitizer).

Rosolem, C.A., Li, Y., Garcia, R.A., 2016. Soil carbon as affected by cover crops under no- till under tropical climate. Soil Use Manag 32, 495–503. https://doi.org/10.1111/ sum.12309.

Samberg, L.H., Gerber, J.S., Ramankutty, N., Herrero, M., West, P.C., 2016. Subnational distribution of average farm size and smallholder contributions to global food production. Environ. Res. Lett. 11, 124010. https://doi.org/10.1088/1748-9326/ 11/12/124010.

Santos, C.B., de Pinho Costa, K.A., de Souza, W.F., da Silva, A.G., e Silva, V.C., de Oliveira, I.P., Brandstette, E.V., 2020. Intercropping of sorghum with paiaguas palisadegrass in a crop-livestock integration system for pasture recovery. Aust. J. Crop Sci. 14, 1072–1080. https://doi.org/10.21475/ajcs.20.14.07.p2216.

Shan, Y.H., Johnson-Beebout, S.E., Buresh, R.J., 2008. Crop residue management for lowland rice-based cropping systems in Asia. Adv. Agron. 98, 117–199. https://doi.org/10.1016/S0065-2113(08)00203-4.

Shekhar, M., Shivashankar, E., Pandey, S.K., 2024. Crop rotation and intercropping techniques. Mod. Concept Agron. Ed. ND Glob. Publ. House Httpsdoi Org. 10, 5281. https://doi.org/10.5281/zenodo.11051201.

Small, F.A.A., Raizada, M.N., 2017. Mitigating dry season food insecurity in the subtropics by prospecting drought-tolerant, nitrogen-fixing weeds. Agric. Food Secur 6, 23. https://doi.org/10.1186/s40066-017-0096-6.

Snapp, S.S., Swinton, S.M., Labarta, R., Mutch, D., Black, J.R., Leep, R., Nyiraneza, J., O'Neil, K., 2005. Evaluating cover crops for benefits, costs and performance within cropping system niches. Agron. J. 97, 322–332. https://doi.org/10.2134/ agronj2005.0322a.

Tanveer, A., Ikram, R.M., Ali, H.H., 2019. Crop rotation: principles and practices. In: Hasanuzzaman, M. (Ed.), Agronomic Crops. Springer Singapore, Singapore, pp. 1–12. https://doi.org/10.1007/978-981-32-9783-8_1.

R. Core Team, 2025. R Core Team. R: a language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. (https://www. R-project.org/).

Tibesigwa, B., Ntuli, H., Lokina, R., Okumu, B., Komba, C., 2021. Long-rains crops, short- rains crops, permanent crops and fruit crops: the ‘hidden’ multiple season-cropping system for adaptation to rain variability by smallholder farms. J. Environ. Manag. 278, 111407. https://doi.org/10.1016/j.jenvman.2020.111407.

Toler, H.D., Augé, R.M., Benelli, V., Allen, F.L., Ashworth, A.J., 2019. Global meta- analysis of cotton yield and weed suppression from cover crops. Crop Sci. 59, 1248–1261. https://doi.org/10.2135/cropsci2018.10.0603.

Tonitto, C., Ricker-Gilbert, J.E., 2016. Nutrient management in African sorghum cropping systems: applying meta-analysis to assess yield and profitability. Agron. Sustain. Dev. 36, 10. https://doi.org/10.1007/s13593-015-0336-8.

Viaud, P., Heuclin, B., Letourmy, P., Christina, M., Versini, A., Mansuy, A., Chetty, J., Naudin, K., 2023. Sugarcane yield response to legume intercropped: a meta-analysis. Field Crops Res. 295, 108882. https://doi.org/10.1016/j.fcr.2023.108882.

Viechtbauer, W., 2010. Conducting meta-analyses in r with the metafor package. J. Stat. Softw. 36, 1–48. https://doi.org/10.18637/jss.v036.i03.

Violle, C., Navas, M., Vile, D., Kazakou, E., Fortunel, C., Hummel, I., Garnier, E., 2007. Let the concept of trait be functional! Oikos 116, 882–892. https://doi.org/10.1111/ j.0030-1299.2007.15559.x.

Wagg, C., van Erk, A., Fava, E., Comeau, L.-P., Mitterboeck, T.F., Goyer, C., Li, S., McKenzie-Gopsill, A., Mills, A., 2021. Full-season cover crops and their traits that promote agroecosystem services. Agriculture 11, 1–26. https://doi.org/10.3390/ agriculture11090830.

Wakjira, M.T., Peleg, N., Anghileri, D., Molnar, D., Alamirew, T., Six, J., Molnar, P., 2021. Rainfall seasonality and timing: implications for cereal crop production in Ethiopia. Agric. For. Meteor. 310, 108633. https://doi.org/10.1016/j. agrformet.2021.108633.

Weinert, C., de Sousa, R.O., Bortowski, E.M., Campelo, M.L., da Silva Pacheco, D., dos Santos, L.V., Deuner, S., Valente, G.B., Matos, A.B., Vargas, V.L., 2023. Legume winter cover crop (Persian clover) reduces nitrogen requirement and increases grain yield in specialized irrigated hybrid rice system. Eur. J. Agron. 142, 126645. https:// doi.org/10.1016/j.eja.2022.126645.

Wood, S.A., Karp, D.S., DeClerck, F., Kremen, C., Naeem, S., Palm, C.A., 2015. Functional traits in agriculture: agrobiodiversity and ecosystem services. Trends Ecol. Evol. 30, 531–539. https://doi.org/10.1016/j.tree.2015.06.013.

Yemadje, P.L., Akplo, T.M., Imorou, L., Sekloka, E., Tittonell, P., 2025a. No-tillage and intercropping improve the yield and profitability of maize-cotton rotations in Northern Benin. Exp. Agric. https://doi.org/10.1017/S0014479725100136.

Yemadje, P.L., Tovihoudji, P.G., Koussihouede, H., Imorou, L., Balarabe, O., Boulakia, S., Sekloka, E., Tittonell, P., 2025b. Reducing initial cotton yield penalties in a transition to conservation agriculture through legume cover crop cultivation–evidence from Northern Benin. Soil Tillage Res. 245, 106319. https:// doi.org/10.1016/j.still.2024.106319.

Yousefi, M., Dray, A., Ghazoul, J., 2024. Assessing the effectiveness of cover crops on ecosystem services: a review of the benefits, challenges, and trade-offs. Int. J. Agric. Sustain. 22, 2335106. https://doi.org/10.1080/14735903.2024.2335106.

Yu, Y., Stomph, T.-J., Makowski, D., van Der Werf, W., 2015. Temporal niche differentiation increases the land equivalent ratio of annual intercrops: a meta- analysis. Field Crops Res. 184, 133–144. https://doi.org/10.1016/j.fcr.2015.09.010.

Zailan, M.Z., Yaakub, H., Jusoh, S., 2018. Yield and nutritive quality of napier (Pennisetum purpureum) cultivars as fresh and ensiled fodder. J. Anim. Plant Sci. 28, 63–72.

Zhao, J., Chen, J., Beillouin, D., Lambers, H., Yang, Y., Smith, P., Zeng, Z., Olesen, J.E., Zang, H., 2022. Global systematic review with meta-analysis reveals yield advantage of legume-based rotations and its drivers. Nat. Commun. 13, 4926. https://doi.org/ 10.1038/s41467-022-32464-0.

Zuur, A.F., Ieno, E.N., Walker, N., Saveliev, A.A., Smith, G.M., 2009. Mixed Effects Models and Extensions in Ecology with R, Statistics for Biology and Health. Springer New York, New York, NY. https://doi.org/10.1007/978-0-387-87458-6.