# Minimum tillage reduces variability and economic risks in cotton-maize rotations in Northern Benin

Tobi Moriaque Akplo, Pierrot Lionel Yemadje, Lucien Imorou, Bettina Sanni, Stéphane Boulakia, Emmanuel Sêkloka, Pablo Tittonell

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Tobi Moriaque Akplo, Pierrot Lionel Yemadje, Lucien Imorou, Bettina Sanni, Stéphane Boulakia, et al. Minimum tillage reduces variability and economic risks in cotton-maize rotations in Northern Benin. Field Crops Research, 2025, 324, pp.109795. 10.1016/j.fcr.2025.109795. hal-05182993

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# Minimum tillage reduces variability and economic risks in cotton-maize rotations in Northern Benin

Tobi Moriaque Akplo<sup>a, *,</sup>, Pierrot Lionel Yemadje<sup>b, c, d, **</sup>, Lucien Imorou<sup>b</sup>, Bettina Sanni<sup>b</sup>, Stéphane Boulakia<sup>d, e</sup>, Emmanuel Sekloka<sup>b, f</sup>, Pablo Tittonell<sup>d, e, g</sup>

## ARTICLE INFO

**Keywords:**

Regenerative agriculture

Conservation agriculture

Profitability

Cotton-maize rotation

Strip-till

## ABSTRACT

**Context:** In sub-Saharan Africa, farmers are still perceiving minimum or no tillage to come at the expense of short-term economic profits.

**Objective:** The study aimed to evaluate the effect of strip-till one-pass (ST) and direct seeding (DS) on crop yield and profitability within a cotton-maize rotation system, in comparison with conventional tillage (CT) and strip till plus hoeing and ridging (STHR), as a farmer adaptation of minimum tillage.

**Methods:** An experiment was conducted in a split-plot design in 2022 and 2023 in northern Benin. Agronomic performance and economical profitability of cotton and maize were measured.

**Results:** Yield penalties were observed in both years with ST and DS while STHR resulted in a 4-9 % increase in cotton yield with respect to CT. The highest number of bolls per cotton plant was observed with ST and DS. Compared to CT, STHR led to a 3-8 % increase in gross income and a 20-70 % increase in gross margin for cotton. Regarding maize, the STHR resulted in an increase in gross income and margin by 13 % and 27 %, respectively, in comparison to CT in 2022. In 2023, the later exhibited the highest gross income and margin.

**Conclusions:** In our case, STHR appears to be reasonable compromise to minimize yield penalties while increasing profitability in an early transition to Conservation Agriculture.

**Implications or significance:** Although STHR led to improved performance, it resulted in extensive soil disturbance in the subsequent year. Continued research efforts are needed to refine farmer-targeted CA practices in cotton-based cropping systems of Benin.

## 1. Introduction

Soil degradation is a significant global issue in the twenty-first century, with particularly severe effects in tropical and subtropical regions (Lal, 2015; Keesstra et al., 2016). It has a significant impact on the livelihoods of the majority of the population who rely on agriculture for

both food and income (Yemadje et al., 2022). Forest destruction, land overexploitation, and unsuitable agricultural practices have contributed to significant soil degradation (Avakoudjo et al., 2021). In sub-Saharan Africa, agricultural management decisions such as the choice and sequencing of crop rotations, the use or not of mineral and organic fertilizers, the frequency of cultivation, and tillage practices, have a

**Abbreviations:** CT, Conventional tillage; ST, Strip-till; STHR, Strip-till + plus hoeing and ridging; DS, Direct seeding; GI, Gross income; GM, Gross margin; RL, Return to labour; BCR, Benefit-to-Cost ratio; NB/P, Number of boll per plant; BW, Seed-cotton mass per boll; TGW, 1000-grain weight; NG/cob, number of grains per cob.

<footnote><sup>a</sup> Research Unity of Soil Microbiology and Soil Conservation, Laboratory of Soil Sciences, Faculty of Agronomic Sciences, University of Abomey-Calavi, Abomey-Calavi, Benin</footnote>

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

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

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

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

<footnote><sup>f</sup> University of Parakou, Faculty of Agronomy, Department of Science and Techniques of Plant Production (D/STPV), PO Box 123, Parakou, Benin</footnote>

<footnote><sup>g</sup> Groningen Institute of Evolutionary Life Sciences, Groningen University, PO Box 11103, Groningen 9700 12 CC, the Netherlands</footnote>

<footnote>* Correspondence to: UR-MEMCES/LSS, Faculty of Agronomic Sciences, University of Abomey-Calavi, Abomey-Calavi, Benin.</footnote>

<footnote>** Corresponding author at: Institute of Cotton Research (IRC), 01 PO Box 175, Cotonou, Benin.</footnote>

<footnote>E-mail addresses: moriaqueakplo@gmail.com (T.M. Akplo), pierrot-lionel.yemadje@cirad.fr (P.L. Yemadje).</footnote>
significant impact on primary productivity and the stabilization of organic carbon in the soil (Kintché et al., 2010; Rahman et al., 2020; Topa et al., 2021; Githongo et al., 2021; Omulo et al., 2022). There is an urgent need to transition towards more sustainable soil management practices to halt the extreme degradation of soils in Sub-Saharan Africa (SSA). Also as smallholder farmers indicate, such a need is felt strongly in intensive cash crop cultivation, as in a cotton based system of northern Benin, where the high cost of inputs, soil degradation and climatic variability result in elevated economic risk (Dossouhoui et al., 2023).

Cotton production is a significant contributor to the economy of West Africa, accounting for nearly 30 % of exports and contributing 7 % to the Gross Domestic Product (Soumaré et al., 2021). In recent years, Benin has increased its use of external inputs, such as mineral fertilizers and pesticides, resulting in a 65 % increase in production and an average 31 % yield increase (DSA, 2021). As these practices have earned the country the position of the top producer in West Africa, recent studies have shown that the soils in the cotton production basins are severely degraded (Amonmide et al., 2019). In West Africa, farmers grow cotton as the first crop in a rotation with food crops, mainly maize (Ripoche et al., 2015). Several factors such as inappropriate tillage, monoculture, over-exploitation of land, inadequate use of chemical inputs, the burning of crop residues, and the lack of biomass input to soil characterize the cotton cropping systems and are key factors responsible for soil fertility decline as reported by recent studies (Hermann et al., 2016; Dossouhoui et al., 2025).

In Benin, cotton is primarily produced in the central and northern regions, which have tropical ferruginous soils and a rainfall range of 900–1200 mm. The tropical ferruginous soils (Acrisols or Lixisols) occupy 60 % of the total surface area of Benin (Agossou, 1983) and are affected by declining soil fertility (Igué et al., 2018). These soils are mostly formed on Embréchite with notable deficiencies in nutrients (N, P and K), organic matter, and are prone to water erosion in Central Benin and both water and wind erosion in the north (Youssou and Lawani, 2000). Cultivating these soils requires special precautions in terms of soil management, nutrient management, crop diversification, and organic matter management. These precautions are not guaranteed by current practices.

Conservation agriculture (CA) practices are seen as an alternative to traditional agricultural practices for improving food security and reducing soil degradation caused by agriculture (Akplo et al., 2024; Farooq and Siddique, 2015; Badgley et al., 2007). CA has the potential to halt soil degradation and even restore soil productivity in the long term in SSA (Atakoun et al., 2023; Omulo et al., 2022; Thierfelder and Mhlanga, 2022; Yemadje et al., 2022; Martinsen et al., 2019; Saito et al., 2010). CA involves changing conventional farming practices and is characterized by minimal soil disturbance, diversified crop rotations, and continuous soil cover to reduce soil and environmental degradation while sustaining crop production (Faroog and Siddique, 2015).

Strip-till is a conservation tillage technique that combines intensive tillage in a narrow strip in which crops are sown and no-tillage between crop rows, using specialised machinery. The technique leaves the majority of the field undisturbed and covered with crop residues (Potratz et al., 2020). A minimum of 50–75 % of the soil is covered with plant residues (Jaskulska et al., 2020; Townsend et al., 2016). Specialised machines now enable the implementation of strip-till one-pass cultivation, suitable for crops with wide (Idowu et al., 2019), medium (Jaskulska et al., 2018) and narrow (Jaskulska et al., 2018) row spacing. It combines the advantage of conventional tillage, plough tillage, and no-till systems. Studies comparing strip-till, conventional till, and no-till found that strip-till and conventional till increased maize grain yield compared to no-till (Licht and Al-Kaisi, 2005; Temesgen et al., 2012; Vetsch et al., 2007). In northern Benin, Yemadje et al. (2025) reported similar levels water productivity of cotton and maize under conventional, strip tillage and no-tillage, with strip tillage exhibiting an average yield penalty of 9.7 % compared to conventional tillage. Pöhlitz et al.

(2019), following traditional physical measurements and applying computer tomography, concluded that soil properties in the strip rows (strip-till) and those after tillage with mulching on the surface were similar. Several authors (Omulo et al., 2022; Jat et al., 2019; Thierfelder et al., 2016) have highlighted the economic benefits of CA, such as fuel savings, increased work productivity, and profitability.

Farmers in SSA hesitate to adopt CA due to the initial yield penalties they may experience (Tittonell et al., 2012). The integration of CA into smallholder farming systems must address existing constraints without creating new complex ones (Thierfelder et al., 2013). To address these constraints, a strategy of transitioning from conventional tillage to CA through strip tillage has been implemented (Yemadje et al., 2022). Conventional Tillage (CT) involves flat ploughing to a depth of 20 cm with a tiller equipped with a share plough followed by harrowing or manual ridging of 20–30 cm high (Dayou et al., 2021). In the context of northern Benin, strip tillage or no tillage can be beneficial in addressing soil degradation and crop yield challenges. For instance, Yemadje et al. (2022) reported limited yield penalties under direct seeding (–12 %) and strip tillage (–15 %) compared to conventional tillage after 2 years of practice in tree locations in northern Benin. However, there are major obstacles to the adoption of CA systems in Benin, including cultural ones (Akplo et al., 2019). Smallholder farmers tend to be conservative, relying on methods passed down through generations and deeply rooted in their traditional way of life (Akplo et al., 2022). Farmers in northern Benin practice "hoeing plus ridging", an ancestral practice that involves weed removal and fertilizer incorporation. Despite numerous outreach and information efforts, cotton and maize growers still prefer hoeing plus ridging after strip-tilling. This practice results in extensive soil cultivation in the following year, maintaining a cycle of tillage and delaying cover crop installation.

The objective of this study was to assess the impact of no- and minimum tillage practices on the yield and profitability of maize and cotton within a crop rotation system, in comparison with conventional tillage and strip till plus hoeing and ridging, as a farmer adaptation of minimum tillage. We hypothesized that i) the two-year cumulative effects of direct seeding and strip tillage practices improve crop yields and profitability compared to conventional tillage; ii) that strip tillage, with or without hoeing and ridging, is a promising compromise between conventional and no-tillage for cotton farmers in Northern Benin.

## 2. Methodology

### 2.1. Study site

This study was carried out at Angaradébou (11°70' N latitude and 2°56' E longitude) in the commune of Kandi, located in the cotton-growing zone of northern Benin. This region has a dry tropical climate with a single growing season per year, from May to September (Atakoun et al., 2023). The average annual rainfall ranges from 900 to 1300 mm (Peel et al., 2007). The soil in the region is classified as ferruginous tropical type in the French soil classification system, which corresponds to Acrisols or Lixisols according to the World Reference Base (Baxter, 2007). The soil at the site is clay in texture and acidic pH of 5.6, very low nitrogen content of 0.04 %, very low soil organic carbon content of 0.54 %, and slightly low assimilable phosphorus content of 18.80 ppm (Amonmide et al., 2021). During the 2022 experimental period, there were 61 rain events, resulting in a cumulative rainfall of 1087 mm. In comparison, the 2023 experiment period had 49 rain events and a cumulative rainfall of 1122 mm (Fig. 1). The 2022 rainfall events were well-distributed, with some exceeding 70 mm. However, the distribution of rain events in 2023 appeared uneven, with some days having over 250 mm of rainfall and others having less than 5 mm.

### 2.2. Experimental design

A randomized complete block with a split-plot arrangement with
<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /><figcaption>Fig. 1. Rainfall distribution from sowing to harvest in 2022 and 2023.</figcaption></figure>

three replications was used to evaluate the effect of four soil tillage options on the agronomic performance of maize and cotton in a rotation system over two successive years. Crops were considered the main plot factor, while tillage options were set up in subplots. The experiment was conducted in 2022 and repeated in 2023 in the same plots. Each elementary plot had an area of 175 m² (25 m × 7 m), separated from adjacent ones by 5-m wide grass strips. The studied treatments are described as follows:

* Conventional tillage (CT): flat plowing to a depth of 20 cm with a tiller equipped with a share plow followed by harrowing. The soil surface was not covered;

* Direct seeding (DS): no tillage and no soil surface cover;

* Strip-Tillage (ST): ripping tillage and 25 % of the soil surface covered with crop residues from the previous growing seasons.

* Strip-Tillage plus hoeing and ridging (STHR): ripping tillage and 25 % of the soil surface covered with crop residues from the previous growing seasons. Weeding and ridging was carried out 45 days after sowing, following local practices.

The experimental plot has a total area of 9000 m² and is uniform in terms of soil type, topography, and cropping history. A biannual sequence of soybeans and maize was practiced on this plot until 2021. At the beginning of the 2021 season, the plow hardpan was broken by a 30 cm sub-soiler pass after clearing.

## 2.3. Crop management

Both terms of the rotation (i.e. maize and cotton) are represented each year, constituting 2 parallel trials. Plots sown with maize in 2022 carried cotton in 2023, and vice versa. The cotton variety ANG 956 (180 maturity-days) and the maize variety 2000 SYNEE were used as recommended by agricultural research for the area. Seeding was performed early (8 days before CT plots) on the ST, STHR and DS plots after important precipitations. Seeding was carried out manually at 0.80 m × 0.30 m with three or four seeds per pocket. Seedlings were separated 15 days after emergence, keeping one plant per pocket, i.e. 41,666 plants per hectare. Plots were prepared with glyphosate (480 g l⁻¹) 15 days before cotton sowing to control soil weeds. Weed control and phytosanitary protection were carried out in accordance with technical recommendations for cotton production in Benin.

Maize was also sown by hand at 0.80 m × 0.40 m with three seeds per planting unit and started at two seedlings per planting unit 15 days after emergence, i.e. 62,500 plants per hectare. Before sowing the maize in June of each year, the plots were worked according to the experimental treatments. Two manual weeding operations were carried out at 15 and 45 days respectively. Phytosanitary protection of the maize was ensured by manual uprooting and burning of attacked plants outside the experimental plots. In both years, the cotton was fertilized with 250 kg ha⁻¹ of N₁₄P₁₈K₁₈S₆B₁ + 50 kg ha⁻¹ of urea (46 %N) (corresponding to 58 kg ha⁻¹ N, 20 kg ha⁻¹ P, 38.75 kg ha⁻¹ K and 3 kg ha⁻¹ S) while 200 kg ha⁻¹ N₁₃P₁₇K₁₇S₅B₀.₅Zn₁.₅ + 50 kg ha⁻¹ of urea (46 %
<caption>**Table 2**</caption>

Prices and values used for analysis of cost, gross revenue, and net revenue from cotton and maize production with the different tillage options.

<table><thead><tr><th rowspan="2">Treatments</th><th rowspan="2">Cleaning</th><th rowspan="2">Tillage</th><th rowspan="2">Seed cost</th><th rowspan="2">Sowing</th><th rowspan="2">Fertilizer cost and application</th><th rowspan="2">Herbicide cost and application</th><th rowspan="2">Phytosanitary products</th><th rowspan="2">Hoeing plus ridging</th><th rowspan="2">Weeding</th><th rowspan="2">Harvest</th><th rowspan="2">Total operating cost</th><th rowspan="2">Income</th></tr><tr><th>US$/ha</th><th>US$/kg</th></tr></thead><tbody><tr><td><strong>Cotton</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CT</td><td>16</td><td>66</td><td>0</td><td>23</td><td>131</td><td>39</td><td>161</td><td>16</td><td>49</td><td>74</td><td>575</td><td>0.49</td></tr><tr><td>DS</td><td>16</td><td>0</td><td>0</td><td>23</td><td>131</td><td>39</td><td>161</td><td>0</td><td>49</td><td>74</td><td>493</td><td>0.49</td></tr><tr><td>ST</td><td>16</td><td>41</td><td>0</td><td>23</td><td>131</td><td>39</td><td>161</td><td>0</td><td>49</td><td>74</td><td>534</td><td>0.49</td></tr><tr><td>STHR</td><td>16</td><td>41</td><td>0</td><td>23</td><td>131</td><td>39</td><td>161</td><td>16</td><td>49</td><td>74</td><td>551</td><td>0.49</td></tr><tr><td><strong>Maize</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CT</td><td>16</td><td>66</td><td>33</td><td>20</td><td>131</td><td>16</td><td>0</td><td>16</td><td>49</td><td>43</td><td>390</td><td>0.33</td></tr><tr><td>DS</td><td>16</td><td>0</td><td>33</td><td>20</td><td>131</td><td>16</td><td>0</td><td>0</td><td>49</td><td>43</td><td>308</td><td>0.33</td></tr><tr><td>ST</td><td>16</td><td>41</td><td>33</td><td>20</td><td>131</td><td>16</td><td>0</td><td>0</td><td>49</td><td>43</td><td>349</td><td>0.33</td></tr><tr><td>STHR</td><td>16</td><td>41</td><td>33</td><td>20</td><td>131</td><td>16</td><td>0</td><td>16</td><td>25</td><td>43</td><td>341</td><td>0.33</td></tr></tbody></table>

× tillage interaction effect for data, we separately fitted a model for each year (2022 and 2023), where tillage was considered as a fixed factor and replicates as a random factor. Significant fixed effects were further dissected by extracting means and performing Tukey's Honestly Significant Difference pairwise comparisons with an alpha of 0.05. A T-Student test was performed to compare the examined years. All these tests were performed within the R programming environment (R. Core Team, 2021).

# 3. Results

## 3.1. Cotton and maize yield

The average seed-cotton yield obtained in 2023 was 47 % higher than that obtained in 2022, whereas the maize yield was 48 % higher in 2022 compared to 2023. Significant differences in seed-cotton yield and maize grain yield were observed across tillage options in both years (p < 0.05) (Table 3). Cotton yields under STHR were on average 9 % and 4 % greater than CT respectively in 2022 and 2023 (cf. dots on or

above the STHR 1:1 line in Fig. 2). In contrast, ST and DS exhibited yield penalties relative to CT in both years (dots below the 1:1 line in Fig. 2). For maize, STHR exhibited the highest yield, while DS and ST had similar maize grain yield to CT in 2022. In 2023, STHR, ST and DS showed frequent yield penalties in terms of maize yield relative to CT (Fig. 2).

## 3.2. Yield components

Significant differences were observed for cotton Number of Boll per plant (NB/P) across tillage options (p < 0.05), but no significant difference was observed for seed-cotton weight per boll (Table 4, p > 0.05). In 2022, ST and STHR had the highest NB/P at 10.2 and 9.9 bolls, respectively with the order being ST > STHR > CT > DS. In 2023, the highest NB/P was observed with DS. Maize cob length and grain number per cob did no differ significantly with tillage options (p > 0.05), however maize cob width varied significantly with tillage options for both years. CT had the highest cob width for both years while ST, STHR and DS had statistically similar cob width. Moreover, the average seed-cotton mass per boll, maize cob length and width as well as the number of grains per cob were higher in 2022 compared to 2023.

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

Crops (Cotton and maize) yields (Mean ± standard deviation) as affected by tillage options.

<table><thead><tr><th>Year</th><th>Tillage options</th><th>Seed-cotton yield (kg ha<sup>-1</sup>)</th><th>Mean relative yields (%)</th><th>Maize grain yield (kg ha<sup>-1</sup>)</th><th>Mean relative yields (%)</th></tr></thead><tbody><tr><td rowspan="7">2022</td><td>CT</td><td>1187 ± 9ab</td><td></td><td>4811 ± 189b</td><td></td></tr><tr><td>DS</td><td>1097 ± 45b</td><td>-8</td><td>4739 ± 232b</td><td>-1</td></tr><tr><td>ST</td><td>1012 ± 17b</td><td>-15</td><td>4946 ± 114b</td><td>3</td></tr><tr><td>STHR</td><td>1289 ± 39a</td><td>9</td><td>5445 ± 136a</td><td>13</td></tr><tr><td>Mean</td><td>1146B</td><td></td><td>4985A</td><td></td></tr><tr><td>CV</td><td>2.87</td><td></td><td>12.05</td><td></td></tr><tr><td>p-values</td><td>&lt; 0.001</td><td></td><td>0.0267</td><td></td></tr><tr><td rowspan="7">2023</td><td>CT</td><td>1742 ± 26a</td><td></td><td>3869 ± 189a</td><td></td></tr><tr><td>DS</td><td>1522 ± 43c</td><td>-13</td><td>2832 ± 176c</td><td>-27</td></tr><tr><td>ST</td><td>1657 ± 52b</td><td>-5</td><td>3312 ± 147bc</td><td>-14</td></tr><tr><td>STHR</td><td>1805 ± 69a</td><td>4</td><td>3489 ± 184ab</td><td>-10</td></tr><tr><td>Mean</td><td>1682A</td><td></td><td>3375B</td><td></td></tr><tr><td>CV</td><td>4.23</td><td></td><td>17.97</td><td></td></tr><tr><td>p-values</td><td>0.0002</td><td></td><td>0.0016</td><td></td></tr></tbody></table>

CT = Conventional Tillage, ST = Strip-till, STHR = Strip-till plus hoeing and ridging, DS = Direct Seeding. In given year and for given parameter, values with the same lower-case letter are not significantly different at p < 0.05. p-values = probability of significance.

## 3.3. Above- and belowground biomass

The performance of minimum tillage to restore soil organic matter depends strongly on its ability to promote above and belowground biomass production. Significant differences in above and belowground biomass were observed across tillage options for cotton in both years (p < 0.05, Table 5). STHR and ST in 2022 and CT in 2023, averaged higher cotton aboveground biomass. The cotton belowground biomass was higher under STHR in both years. Significant differences were observed for maize aboveground biomass in 2022 and root biomass in 2023 (p < 0.05, Table 5). The highest maize aboveground biomass in 2022 was recorded with ST (7417 kg ha<sup>-1</sup>) while DS yielded more maize belowground biomass (Table 5).

## 3.4. Economic analysis

### 3.4.1. Gross income and gross margin

The gross income and gross margin obtained for cotton in 2023 were significantly higher than those obtained in 2022. Conversely, the gross income and gross margin for maize were lower in 2023 than in 2022 (Table 6). Moreover, significant differences were observed among tillage options across years (p < 0.0001). STHR exhibited, on average, 8 % and 4 % greater gross income and 70 % and 20 % greater gross margin compared to CT respectively in 2022 and 2023 (Table 6). Cotton gross margins were close to zero or negative in 2022 across tillage treatments, except under STHR, which exhibited higher margins than CT (Fig. 3). In
<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Relative seed cotton and grain maize yields (kg ha⁻¹) under Direct seeding (DS), Strip-till (ST), and Strip-till plus hoeing and ridging (STHR) compared to Conventional tillage (CT). Dashed lines represent the 1:2; 1:1 and 2:1 lines.</figcaption></figure>

<table><caption>**Table 4**

Cotton and maize yield components by treatment and year (mean ± standard deviation).</caption><thead><tr><th rowspan="2">Year</th><th rowspan="2">Options</th><th colspan="2">Cotton</th><th colspan="4">Maize</th></tr><tr><th>NB/P</th><th>BW (g/boll)</th><th>TGW (g)</th><th>Cob length (cm)</th><th>Cob Width (cm)</th><th>NG/ cob (#/cob)</th></tr></thead><tbody><tr><td rowspan="5">2022</td><td>CT</td><td>8.0 ± 0.3b</td><td>4.5 ± 0.2</td><td>295 ± 6.3c</td><td>16.9 ± 0.6</td><td>3.9 ± 0.2a</td><td>422 ± 20.5</td></tr><tr><td>DS</td><td>7.6 ± 0.5b</td><td>4.1 ± 0.1</td><td>306 ± 5.8bc</td><td>16.7 ± 0.1</td><td>3.3 ± 0.1b</td><td>448 ± 14.7</td></tr><tr><td>ST</td><td>10.2 ± 0.4a</td><td>4.3 ± 0.3</td><td>310 ± 3.1ab</td><td>15.6 ± 0.6</td><td>3.1 ± 0.1b</td><td>411 ± 17.0</td></tr><tr><td>STHR</td><td>9.9 ± 0.0a</td><td>4.5 ± 0.1</td><td>321 ± 3.3a</td><td>16.8 ± 0.6</td><td>3.4 ± 0.2b</td><td>410 ± 12.7</td></tr><tr><td><strong>Mean</strong></td><td><strong>8.9B</strong></td><td><strong>4.3A</strong></td><td><strong>308B</strong></td><td><strong>16.5A</strong></td><td><strong>3.5A</strong></td><td><strong>423A</strong></td></tr><tr><td>CV</td><td>6.8</td><td>8.0</td><td>5.5</td><td>13.8</td><td>17.9</td><td>11.7</td></tr><tr><td>p-values</td><td>0.0014</td><td>0.457</td><td>0.0039</td><td>0.3966</td><td>0.0043</td><td>0.3361</td></tr><tr><td rowspan="5">2023</td><td>CT</td><td>12.7 ± 0.3ab</td><td>3.3 ± 0.2</td><td>268 ± 3.9</td><td>14.8 ± 0.6</td><td>3.2 ± 0.1a</td><td>406 ± 26.8</td></tr><tr><td>DS</td><td>13.6 ± 0.2a</td><td>3.2 ± 0.2</td><td>268 ± 3.2</td><td>16.8 ± 0.6</td><td>2.9 ± 0.1ab</td><td>341 ± 22.9</td></tr><tr><td>ST</td><td>11.9 ± 0.1b</td><td>2.9 ± 0.3</td><td>279 ± 4.3</td><td>15.4 ± 0.6</td><td>2.9 ± 0.1b</td><td>392 ± 16.3</td></tr><tr><td>STHR</td><td>12.8 ± 0.4ab</td><td>3.6 ± 0.3</td><td>279 ± 5.5</td><td>14.6 ± 0.6</td><td>3.0 ± 0.1ab</td><td>402 ± 16.1</td></tr><tr><td><strong>Mean</strong></td><td><strong>12.8A</strong></td><td><strong>3.3B</strong></td><td><strong>273A</strong></td><td><strong>14.8A</strong></td><td><strong>3.0A</strong></td><td><strong>386B</strong></td></tr><tr><td>CV</td><td>4.04</td><td>13.9</td><td>5.5</td><td>15.2</td><td>13.0</td><td>16.4</td></tr><tr><td>p-values</td><td>0.0274</td><td>0.388</td><td>0.077</td><td>0.7384</td><td>0.0155</td><td>0.1269</td></tr></tbody></table>

CT = Conventional Tillage, ST = Strip-till, STHR = Strip-till plus hoeing and ridging, DS = Direct Seeding, NB/P = Number of Boll per plant, BW = Seed-cotton mass per boll, TGW = 1000-grain weight; NG/cob = number of grains per cob. In given year and for given parameter, values with the same lower-case letter are not significantly different at p < 0.05. p-values = probability of significance
<caption>**Table 5**</caption>

Cotton and maize above-ground and Below-ground biomass (mean ± standard error).

<table><thead><tr><th rowspan="2">Year</th><th rowspan="2">Tillage options</th><th colspan="2">Cotton</th><th colspan="2">Maize</th></tr><tr><th>Above-ground biomass (kg ha<sup>-1</sup>)</th><th>Below-ground biomass (kg ha<sup>-1</sup>)</th><th>Above-ground biomass (kg ha<sup>-1</sup>)</th><th>Below-ground biomass (kg ha<sup>-1</sup>)</th></tr></thead><tbody><tr><td rowspan="7">2022</td><td>CT</td><td>7313 ± 830ab</td><td>1038 ± 98b</td><td>5689 ± 523b</td><td></td></tr><tr><td>DS</td><td>6843 ± 1140b</td><td>1224 ± 543ab</td><td>6906 ± 315 ab</td><td></td></tr><tr><td>ST</td><td>8437 ± 556a</td><td>1035 ± 44b</td><td>7417 ± 426a</td><td></td></tr><tr><td>STHR</td><td>8643 ± 702a</td><td>1461 ± 123a</td><td>7067 ± 192ab</td><td></td></tr><tr><td>Mean</td><td><strong>7809A</strong></td><td><strong>1207B</strong></td><td><strong>6769A</strong></td><td></td></tr><tr><td>CV</td><td>19.02 ± 0.0276</td><td>23.19 ± 0.0089</td><td>17.05 ± 0.0193</td><td></td></tr><tr><td>p-values</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="7">2023</td><td>CT</td><td>7317 ± 451a</td><td>1775 ± 74ab</td><td>5228 ± 314 ± 100b</td><td></td></tr><tr><td>DS</td><td>3875 ± 369c</td><td>1540 ± 152b</td><td>5220 ± 244 ± 103a</td><td></td></tr><tr><td>ST</td><td>5186 ± 435bc</td><td>1538 ± 152b</td><td>4733 ± 266 ± 115ab</td><td></td></tr><tr><td>STHR</td><td>6265 ± 570ab</td><td>1954 ± 145a</td><td>5122 ± 350 ± 97ab</td><td></td></tr><tr><td>Mean</td><td><strong>5688B</strong></td><td><strong>1713A</strong></td><td><strong>5076B</strong></td><td><strong>1483</strong></td></tr><tr><td>CV</td><td>30.14 ± 0.0006</td><td>20.47 ± 0.0478</td><td>17.55 ± 0.3361</td><td>21.08 ± 0.0471</td></tr><tr><td>p-values</td><td></td><td></td><td></td><td></td></tr></tbody></table>

CT = Conventional Tillage, ST = Strip-till, STHR = Strip-till + plus hoeing and ridging, DS = Direct Seeding. In given year and for given parameter, values with the same lower-case letter are not significantly different at p < 0.05. ns = non-significant, p-values = probability of significance. ns = non-significant, p-values = probability of significance

<caption>**Table 6**</caption>

Gross income and gross margin for cotton and maize under the tillage options.

<table><thead><tr><th rowspan="2">Year</th><th rowspan="2">Tillage option</th><th colspan="2">Cotton</th><th colspan="2">Maize</th></tr><tr><th>Gross income</th><th>Gross margin</th><th>Gross income</th><th>Gross margin</th></tr><tr><td></td><td></td><td colspan="4" style="text-align: center;">US$ ha<sup>-1</sup></td></tr></thead><tbody><tr><td rowspan="7">2022</td><td>CT</td><td>583ab</td><td>43b</td><td>1104b</td><td>716b</td></tr><tr><td>DS</td><td>540b</td><td>46ab</td><td>1088b</td><td>781ab</td></tr><tr><td>ST</td><td>498b</td><td>-37c</td><td>1135b</td><td>788ab</td></tr><tr><td>STHR</td><td>624a</td><td>73a</td><td>1249a</td><td>910a</td></tr><tr><td>Mean</td><td><strong>561B</strong></td><td><strong>31B</strong></td><td><strong>1144A</strong></td><td><strong>799A</strong></td></tr><tr><td>CV</td><td>8.28</td><td>8.55</td><td>13.89</td><td>17.26</td></tr><tr><td>p-values</td><td>&lt; .0001</td><td>&lt; .0001</td><td>0.0111</td><td>0.0204</td></tr><tr><td rowspan="7">2023</td><td>CT</td><td>857ab</td><td>281ab</td><td>888a</td><td>516 a</td></tr><tr><td>DS</td><td>749c</td><td>255b</td><td>650c</td><td>344b</td></tr><tr><td>ST</td><td>815b</td><td>281ab</td><td>760bc</td><td>413ab</td></tr><tr><td>STHR</td><td>888a</td><td>362a</td><td>801ab</td><td>462ab</td></tr><tr><td>Mean</td><td><strong>827A</strong></td><td><strong>295A</strong></td><td><strong>775B</strong></td><td><strong>434B</strong></td></tr><tr><td>CV</td><td>10.65</td><td>4.25</td><td>12.39</td><td>32.12</td></tr><tr><td>p-values</td><td>0.0005</td><td>&lt; .0001</td><td>0.0282</td><td>&lt; .0001</td></tr></tbody></table>

CT = Conventional Tillage, ST = Strip-till, STHR = Strip-till plus hoeing and ridging, DS = Direct Seeding. In given year and for given parameter, values with the same lower-case letter are not significantly different at p < 0.05. p-values = probability of significance

2023, gross margins were generally greater under CT than minimum tillage, except again under STHR (Fig. 3). Gross income and margin were significantly greater for maize than cotton in 2022, while the opposite was observed in 2023 (Table 6). The cost of growing cotton under DS, ST and STHR decreased by 14 %, 7 % and 8 % compared to CT. In the case of maize, respective changes in production costs were -20, -10 and -12 % (Table 1).

### 3.4.2. Labour productivity, return to labour and benefit-cost-ratio

Conventional tillage and STHR exhibited significantly higher labour productivity and return to labour for cotton than the other tillage options in 2023 (27 kg ha⁻¹ and 24 kg ha⁻¹ respectively for CT and STHR) (Fig. 4). No significant difference was observed between treatments regarding the labour productivity and return to labour for cotton in 2022 (Fig. 4). In the case of maize, the labour was more productive with ST (156 kg ha⁻¹), STHR (154 kg ha⁻¹) and CT (152 kg ha⁻¹) in 2022 while CT showed larger labour productivity for maize in 2023 (Fig. 4). The return to labour for maize was not significantly different between the treatments in 2022, and significantly greater under CT in 2023. The benefit-cost-ratio was significantly greater with CT and STHR in 2023 for cotton, with DS and ST in 2022 and with CT in 2023 (Fig. 4).

## 4. Discussion

This study aimed to provide insights into the relationship between tillage options and agronomic and economic outcomes over two consecutive years within a cotton-maize crop rotation system. The results show that during the first two years of a transition towards conservation agriculture, be it towards minimum or no tillage, yield penalties persist affecting the economic performance of both cotton and maize. Minimum tillage through strip-till appeared as a promising compromise between no- and conventional tillage, provided that it is combined with ridging and hoeing during the season (i.e., the STHR treatment). The latter allowed reducing economic risk variability, producing positive gross margins also during 2022, when the rest of the treatments led to negative margins in most cases.

### 4.1. Impact of tillage systems on crop productivity

The results shed light on the pivotal role that tillage practices play in influencing various aspects of agricultural production (Gathala et al., 2015; Singh et al., 2021). While strip-till with hoeing and ridging (STHR) exhibited superior performance in terms of seed-cotton and maize grain yields in the year 2022, both conventional tillage and STHR exhibited the highest seed-cotton yields in the subsequent year (2023), indicating the influence of changing environmental conditions on the effectiveness of tillage practices. These results are not surprising, as most studies have reported that the transition from conventional towards conservation tillage is characterized by yield penalties in the first years of adoption (Tittonell et al., 2012; Corbeels et al., 2014; Yemadje et al., 2022). In the case of cotton, literature has shown that it takes at least 3–8 years to observe an increase in yield after conservation agriculture has been implemented (Nyakatawa et al., 2001; Boquet et al., 2004; Bau-dron et al., 2012). On the other hand, Thierfelder et al. (2015) reported that no-till practices often resulted in initially smaller maize yields than conventional but increased thereafter. This situation was observed in this study as the cotton yield penalty under ST and DS decreased in 2023 compared to 2022.

The yield penalties observed with DS and ST compared to STHR and CT can be explained by the immobilization of soil nutrients, poor germination, increased competition of weeds, and poor drainage that can result from shifting from conventional to conservation agriculture (Giller et al., 2009; Sommer et al., 2014; Charles et al., 2020). In contrast, conventional tillage leads to oxidative conditions in the soil that accelerate nutrient release and their assimilation by the crop, while also burying weeds, fertilizer and incorporating surface organic matter into the soil, making the soil easier for roots to penetrate (Yemadje et al., 2022). In the STHR tillage system, hoeing and ridging were performed 45 days after sowing. This practice destroys weeds, buries fertilizer, does earthing up and creates shallow ditches between the rows. The ditches intercept and retain running rainwater. In addition, earthing up supports the plants, aerates the rooting zone and decreases resistance to growing roots. This would explain the performance observed under STHR compared with ST and DS, and the insistence of local farmers to continue
<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Relative gross income and gross margin (US$ ha⁻¹) for cotton and maize under Direct seeding (DS), Strip-till (ST), and Strip-till plus hoeing and ridging (STHR) compared to Conventional tillage (CT). Dashed lines represent the 1:2; 1:1 and 2:1 lines.</figcaption></figure>

practicing it even under minimum tillage. Nevertheless, this practice leads to a return to tillage to make the soil flat to be strip tillied the next year.

The observed variability in cotton NB/P and maize cob width suggests that tillage practices can have subtle yet significant effects on the components of crop yield. NB/P in cotton and cob width in maize are influenced by various factors, including genetics, environmental conditions, and management practices (Jumanov et al., 2023). While the study found that tillage options did not significantly affect seed-cotton weight per boll or maize grain number per cob, the differences in NB/P and cob width indicate that tillage can influence plant architecture and resource allocation, as seen also by Sinha et al. (2021). Reduced tillage practices may create conditions that promote more extensive root development (Yemadje et al., 2022), leading to increased nutrient uptake (Malhi et al., 2006) and potentially influencing boll formation in cotton (Reddy et al., 2012) or cob development in maize (van Balen et al., 2023). These findings underscore the importance of holistic agronomic management practices that take into account not only yield outcomes but also the developmental processes and physiological responses of crops to tillage-induced soil conditions (George, 2014). Furthermore, the significant effects of tillage options on above-ground and root biomass underscore their role in shaping crop growth dynamics and nutrient acquisition strategies. The higher above-ground and root biomass observed with STHR, particularly in cotton, suggests its

potential to enhance soil-plant interactions, nutrient cycling, and soil carbon storage.

## 4.2. Impact of tillage systems on economic performance

The gross margin from cotton was close to or below zero under different tillage treatments in 2022, and was lower than maize on average in both years (cf. Fig. 3). Labour productivity, return to labour and the cost-benefit ratio were also much more favourable for maize than cotton, in spite of the year-to-year variation and irrespective of tillage system (cf. Fig. 4). These results question the economic viability of cotton for smallholder farmers. While cotton is a major export crop for Benin, representing 30 % of the total export and 20 % of the national gross domestic product, the prospect for farmers to continue growing cotton is bleak. In fact, farmers in Benin are increasingly switching from cotton to soybean as their main source of income (Kpenavoun Chogou et al., 2018). One of the main reasons for farmers to grow cotton is the access to inputs such as fertilizers and pesticides, which they use partly also on maize and other food crops (Ripoche et al., 2015).

Over the two years, STHR significantly increased the gross margin, labour productivity, benefit-cost ratio and the return to labour for cotton respectively by 39 %; 13 %, 45 % and 41 % compared to CT. This performance underscores its economic viability and potential for enhancing farm profitability and reducing economic risks. Although this was not
<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Variation of Labour productivity, return to labour and benefit-cost-ratio between treatments for cotton and maize in 2022 and 2023. CT: Conventional tillage; DS: Direct seeding; ST: Strip-till; STHR: Strip-till plus hoeing and ridging.</figcaption></figure>

tested in the present study, minimum tillage would allow also planting the crops earlier after the first rains of the season, increasing rainfall use efficiency (Atakoun et al., 2023). CT requires time and labour for ploughing, which may contribute to the observed lower economic performance of cotton over the two years compared to STHR. DS and ST exhibited low economic performances in this study associated with the yield differences observed, given that the sale prices were the same for all treatments. The finding concurs with Sime et al. (2015) who reported higher gross margins on conventional tillage practices than the minimum and no-tillage practices due to yield differences among small-scale farmers in Ethiopia. Negative short-term economic benefits are common in the transition to conservation agriculture (Giller et al., 2009; Grabowski et al., 2014), although the economic benefits of CA are always context and site-specific (Thierfelder et al., 2016).

The yields obtained in this experiment for both cotton and maize were substantially higher than the average yields that farmers obtain in the cotton growing regions of Benin, which are in the order of 1100 kg ha⁻¹ (FAOSTAT, 2024). Despite this, the economic margins for cotton obtained in this experiment were very low to negative in 2022 and generally low in 2023. In the light of this evidence, the production

costs associated with the fertilization and phytosanitary treatments applied in the present study (cf. Tables 1 and 2) are likely prohibitive to smallholder farmers. Further research should focus on agroecological intensification strategies that reduce the dependence on external inputs by increasing resource use efficiency and natural regulation in the agroecosystem through diversification, as seen repeatedly in different contexts (Ditzler et al., 2021; Kebede et al., 2018; Teixeira et al., 2022). On the other hand, research efforts are needed to refine soil fertility management practices to accompany the transition from conventional to minimum tillage. Options such as the application of parkland poudrette localized in the row (0, 2 and 4 t/ha) (e.g. Micheni et al., 2004), facilitated by the early materialization (in April) of the sowing rows - in addition to a mineral fertilization program or the sowing of legumes (Vigna unguiculata, Vigna radiata, Mucuna pruriens, Crotalaria juncea) on the tilled row from mid-April (before the first rains) can be envisaged (Blomme et al., 2022; Otieno et al., 2021).

## 5. Conclusion

We tested the effect of minimum and no-tillage practices on the yield
and profitability of cotton and maize within a crop rotation system in the early phase of the transition to conservation agriculture. Minimum tillage in the form of strip tillage plus hoeing and ridging once during the season showed superior seed-cotton and maize grain yields as well as economic profitability than conventional tillage. However, this approach resulted in extensive soil disturbance in the subsequent year. This is undesirable in the context of agroecological transition, which implies a shift from conventional tillage to minimum soil disturbance. Adoption of conservation tillage systems, such as strip-till one pass and direct sowing, holds promise for enhancing agricultural sustainability, resilience, and profitability in the face of evolving environmental and economic challenges.

While our study underscores the multifaceted nature of the relationship between tillage practices and crop production and economic performance, it spans only two years, which may not capture long-term trends or account for interannual variability in weather patterns and crop performance. Longer-term studies considering other potential factors that could influence agricultural productivity, such as soil type and climate variability, could provide more robust insights into the sustainability and resilience of different tillage systems. Ignoring these factors could limit the generalization of the findings.

## CRediT authorship contribution statement

**Emmanuel Sekloka:** Writing – review & editing, Supervision, Funding acquisition, Conceptualization. **Stéphane Boulakia:** Writing – review & editing, Validation, Supervision, Funding acquisition, Conceptualization. **Bettina Sanni:** Writing – review & editing, Investigation, Data curation. **Lucien Imorou:** Writing – review & editing, Methodology, Investigation, Data curation. **Pierrot Lionel Yemadje:** Writing – review & editing, Writing – original draft, Validation, Funding acquisition, Data curation, Conceptualization. **Tobi Moriaque Akplo:** Writing – review & editing, Writing – original draft, Visualization, Methodology. **Pablo Tittonell:** Writing – review & editing, Visualization, Validation, Methodology, 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

This research was funded by the Benin Cotton Research Institute (IRC), Cotton Interprofessional Association (AIC), and the TAZCO2 project (Transition Agroécologique des Zones Cotonnières du Bénin), which is funded by the Republic of Benin and the French Development Agency (AFD).

## Data availability

Data will be made available on request.

## References

* Agossou, V., 1983. Les sols Béninois et leurs Potentialités Agricoles. Study N°260; Projet Agro-Pédologie.

* Akplo, T.M., Alladassi, F.K., Zoundji, M.C.C., Avakoudjo, J., Houngnandan, P., Dagbénonbakin, D.G., Saïdou, A., Benmansour, M., Fulajtar, E., Amadji, G.L., Azontondé, H.A., Assogbadjo, A.E., Kakaï, R.G., 2022. Impact of conservation tillage on runoff, soil loss, and soil properties on acrisols and ferralsols in central Benin. Can. J. Soil Sci. 102, 659–671. https://doi.org/10.1139/cjss-2021-0080.

* Akplo, T.M., Kouelo Alladassi, F., Houngnandan, P., Azontondé, H.A., Agonvinon, M.S., Bokossa, T.S., 2019. Factors influencing soil erosion control practices adoption in centre of the Republic of Benin: use of multinomial logistic. J. Agric. Sci. 11, 110. https://doi.org/10.5539/jas.v11n17p110.

* Akplo, T.M., Kouelo Alladassi, F., Zoundji, M.C.C., Fulajtar, E., Benmansour, M., Rabesiranana, N., Akinseye, F.M., Houngnandan, P., 2024. Effect of no-tillage on soil

redistribution estimated by beryllium-7, soil moisture, and carbon fractions loss in central Benin. Agrosyst. Geosci. Environ. 7, 1–19. https://doi.org/10.1002/agg2.20452.

* Amonmide, I., Dagbenonbakin, G., Agbangba, C.E., Akponikpe, P., 2019. Contribution to soil fertility level assessment in cotton-based cropping systems in Benin | Contribution à l'évaluation du niveau de fertilité des sols dans les systèmes de culture à base du coton au Bénin. Int. J. Biol. Chem. Sci. 13, 1846–1860.

* Amonmide, I., Guibert, H., Takpa, O., I, Fayalo, D.G., 2021. Note de synthèse des résultats récents d'analyses de sols en zone cotonnière du Bénin.

* Atakoun, A.M., Tovihoudji, P.G., Diogo, R.V.C., Yemadje, P.L., Balarabe, O., Akponikpè, P.B.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.

* Avakoudjo, J., Kouelo Alladassi, F., Kindomihou, V., Akplo, T.M., Agonvinon, M.S., Amadji, G.L., Sinsin, B., 2021. Soil conditions in the “donga” soils in subhumid zone in west Africa. Can. J. Soil Sci. 101, 617–625. https://doi.org/10.1139/cjss-2020-0137.

* Badgley, C., Moghtader, J., Quintero, E., Zakem, E., Chappell, M.J., Avilés-Vázquez, K., Samulon, A., Perfecto, I., 2007. Organic agriculture and the global food supply. Renew. Agric. Food Syst. 22, 86–108. https://doi.org/10.1017/S1742170507001640.

* Baudron, F., Tittonell, P., Corbeels, M., Letourmy, P., Giller, K.E., 2012. Comparative performance of conservation agriculture and current smallholder farming practices in semi-arid Zimbabwe. Field Crops Res. 132, 117–128. https://doi.org/10.1016/j.fcr.2011.09.008.

* Baxter, S., 2007. World Reference Base for Soil Resources. World Soil Resources Report 103. Rome: Food and Agriculture Organization of the United Nations (2006), pp. 132, US$22.00 (paperback). ISBN 92-5-10511-4. Experimental Agriculture, 43, 264–264. (http://doi.org/10.1017/S0014479706394902).

* Blomme, G., Ntamwira, J., Ocimati, W., 2022. Mucuna pruriens, Crotalaria juncea, and chickpea (Cicer arietinum) have the potential for improving productivity of banana-based systems in Eastern Democratic Republic of Congo. Legume Sci. 4, 1–14. https://doi.org/10.1002/leg3.145.

* Boquet, D.J., Hutchinson, R.L., Breitenbeck, G.A., 2004. Long-term tillage, cover crop, and nitrogen rate effects on cotton: plant growth and yield components. Agron. J. 96, 1443–1452. https://doi.org/10.2134/agronj2004.1443.

* Corbeels, M., de Graaff, J., Ndah, T.H., Penot, E., Baudron, F., Naudin, K., Andrieu, N., Chirat, G., Schuler, J., Nyagumbo, I., Rusinamhodzi, L., Traore, K., Mzoba, H.D., Adolwa, I.S., 2014. Understanding the impact and adoption of conservation agriculture in Africa: a multi-scale analysis. Agric. Ecosyst. Environ. 187, 155–170. https://doi.org/10.1016/j.agee.2013.10.011.

* Dayou, E.D., Zokpodo, K.L.B., Atidegla, C.S., Dahou, M.N., Ajav, E.A., Bamgboye, A.I., Glèlè Kakaï, L.R., 2021. Analysis of the use of tractors in different poles of agricultural development in Benin Republic. Heliyon 7. https://doi.org/10.1016/j.heliyon.2021.e06145, 1–8.

* Ditzler, L., van Apeldoorn, D.F., Pellegrini, F., Antichi, D., Bárberi, P., Rossing, W.A.H., 2021. Current research on the ecosystem service potential of legume inclusive cropping systems in Europe. A review. Agron. Sustain. Dev. 41. https://doi.org/10.1007/s13593-021-00678-z.

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

* Dossouhoui, A.I., Yemadje, P.L., Berre, D., Diogo, R.V., Tittonell, P.A., 2025, in press. How does heterogeneity across farms and management practices influence soil fertility in West African cotton farming? Evidence from Benin. Available at SSRN 5018929.

* DSA, 2021. Rapport de Performances du Secteur Agricole au Benin.131p.

* FAOSTAT, 2024. Crops and livestock products data. (https://www.fao.org/faostat/en/#data/QCL) (Accessed on 20 January 2025).

* Farooq, M., Siddique, K.H.M., 2015. Conservation agriculture. Conservation Agriculture, 1–665. https://doi.org/10.1007/978-3-319-11620-4.

* Gathala, M.K., Timsina, J., Islam, M.S., Rahman, M.M., Hossain, M.I., Harun-Ar-Rashid, M., Ghosh, A.K., Krupnik, T.J., Tiwari, T.P., McDonald, A., 2015. Conservation agriculture-based tillage and crop establishment options can maintain farmers’ yields and increase profits in South Asia’s rice-maize systems: Evidence from Bangladesh. Field Crops Res. 172, 85–98. https://doi.org/10.1016/j.fcr.2014.12.003.

* George, T., 2014. Why crop yields in developing countries have not kept pace with advances in agronomy. Glob. Food Secur. 3, 49–58. https://doi.org/10.1016/j.gfs.2013.10.002.

* Giller, K.E., Witter, E., Corbeels, M., Tittonell, P., 2009. Conservation agriculture and smallholder farming in Africa: the heretics’ view. Field Crops Res. 114, 23–34. https://doi.org/10.1016/j.fcr.2009.06.017.

* Githongo, M.W., Kiboi, M.N., Ngetich, F.K., Musafiri, C.M., Muriuki, A., Fliessbach, A., 2021. The effect of minimum tillage and animal manure on maize yields and soil organic carbon in sub-Saharan Africa: A meta-analysis. Environ. Chall. 5, 100340. https://doi.org/10.1016/j.envc.2021.100340.

* Grabowski, P.P., Haggblade, S., Kabwe, S., Tembo, G., 2014. Minimum tillage adoption among commercial smallholder cotton farmers in Zambia, 2002 to 2011. Agric. Syst. 131, 34–44. https://doi.org/10.1016/j.agsy.2014.08.001.

* Hermann, M.B., Moumouni, I., Tokore Orou Mere, S.B.J., 2016. Contribution à l'amélioration des pratiques paysannes de production durable de coton (Gossypium hirsutum) au Bénin: cas de la commune de Banikoara. Int. J. Biol. Chem. Sci. 9, 2401. https://doi.org/10.4314/ijbcs.v9i5.12.
Hunt, R.C., 2000. Labor productivity and agricultural development: boserup revisited. Hum. Ecol. 28, 251-277. https://doi.org/10.1023/A:100707210891.

Idowu, O.J., Sultana, S., Darapuneni, M., Beck, L., Steiner, R., 2019. Short-term conservation tillage effects on corn silage yield and soil quality in an irrigated, arid agroecosystem. Agronomy 9, 1-17. https://doi.org/10.3390/agronomy9080455.

Igué, A.M., Balogoun, I., Saidou, A., Oga, A.C., Ezui, G., Youl, S., Kpagbin, G., Mando, A., Sogbedji, J.M., 2018. Recommendations of fertilizer formulas for the production of the EVDT 97 maize variety in Northern Benin. In: Improving the Profitability, Sustainability and Efficiency of Nutrients Through Site Specific Fertilizer Recommendations in West Africa Agro-Ecosystems. 2. Springer International Publishing, pp. 105-123. https://doi.org/10.1007/978-3-319-58792-9-7.

Jaskulski, I., Gałczewski, L., Piekarczyk, M., Jaskulski, D., 2018. Strip-till technology - a method for uniformity in the emergence and plant growth of winter rapeseed (Brassica napus L.) in different environmental conditions of Northern Poland. Ital. J. Agron. 13, 194-199. https://doi.org/10.4081/ija.2018.981.

Jaskulski, I., Romanceckas, K., Jaskulski, D., Wojewódzki, P., 2020. A strip-till one-pass system as a component of conservation agriculture. Agronomy 10. https://doi.org/10.3390/agronomy10122015.

Jat, R.K., Singh, R.G., Kumar, M., Jat, M.L., Parihar, C.M., Bijarniya, D., Sutaliya, J.M., Jat, M.K., Parihar, M.D., Kakraliya, S.K., Gupta, R.K., 2019. Ten years of conservation agriculture in a rice-maize rotation of Eastern Gangetic Plains of India: Yield trends, water productivity and economic profitability. Field Crops Res. 232, 1-10. https://doi.org/10.1016/j.fcr.2018.12.004.

Jumanov, D.T., Kuziboyev, J.B., Izzatullayev, L.A., 2023. The impact of agrotechnical factors on the productivity of cotton in the context of optimization. BIO Web Conf. 71. https://doi.org/10.1051/bioconf/20237101078.

Kebede, Y., Baudron, F., Bianchi, F., Tittonell, P., 2018. Unpacking the push-pull system: assessing the contribution of companion crops along a gradient of landscape complexity. Agric. Ecosyst. Environ. 268, 115-123. https://doi.org/10.1016/j.agee.2018.09.012.

Keesstra, S.D., Bouma, J., Wallinga, J., Tittonell, P., Smith, P., Cerdà, A.

Montanarella, L., Quinton, J., Papachesky, Y., van der Putten, W.H., Bardgett, R.D., Moolenaar, S., Mol, G., Fresco, L.O., 2016. FORUM paper: the significance of soils and soil science towards realization of the UN sustainable development goals (SDGs). Soil 2, 111-128. https://doi.org/10.5194/soil-2015-88.

Kintché, K., Guibert, H., Sogbedji, J.M., Levèque, J., Tittonell, P., 2019. Carbon losses and primary productivity decline in savannah soils under cotton-cereal rotations in semiarid Togo. Plant Soil 336, 469-484. https://doi.org/10.1007/s11104-010-0500-5.

Kpenavoun Chogou, S., Santos, F., Joseph Hounhouigan, D., 2018. Efficacité Technique des Producteurs de Soja du Bénin Gestion rationnelle des ressources naturelles. Ann. Des. Sci. Agron. 22, 93-110.

Lal, R., 2015. Restoring soil quality to mitigate soil degradation. Sustainable 7, 5875-5895. https://doi.org/10.3390/su7055875.

Licht, M.A., Al-Kaisi, M., 2005. Corn response, nitrogen uptake, and water use in strip-tillage compared with no-tillage and chisel plow. Agron. J. 97, 705-710. https://doi.org/10.2134/agronj2004.0102.

Malhi, S.S., Lemke, R., Wang, Z.H., Chhabra, B.S., 2006. Tillage, nitrogen and crop residue effects on crop yield, nutrient uptake, soil quality, and greenhouse gas emissions. Soil Tillage Res. 90, 171-183. https://doi.org/10.1016/j.still.2005.09.001.

Martinsen, V., Munera-Echeverri, J.L., Obia, A., Cornelissen, G., Mulder, J., 2019. Significant build-up of soil organic carbon under climate-smart conservation farming in Sub-Saharan Acrisols. Sci. Total Environ. 660, 97-104. https://doi.org/10.1016/j.scitotenv.2018.12.452.

Micheni, A.N., Kihanda, F.M., Warren, G.P., Probert, M.E., 2004. Testing the APSIM model with experimental data from the long-term manure experiment at Machang'a (Embu), Kenya. In: Delve, I.R.J., Probert, M.E. (Eds.), Modelling Nutrient Productivity in Tropical Cropping Systems. ACIAR Proc. Australian Centre for International Agricultural Research Canberra, pp. 110-117.

Nyakatawa, E.Z., Reddy, K.C., Lemunyon, J.L., 2001. Predicting soil erosion in conservation tillage cotton production systems using the revised universal soil loss equation (RUSLE). Soil Tillage Res. 57, 213-224. https://doi.org/10.1016/S0167-1987(00)000178-1.

Omulo, G., Birner, R., Köller, K., Simunji, S., Daum, T., 2022. Comparison of mechanized conservation agriculture and conventional tillage in Zambia: a short-term agronomic and economic analysis. Soil Tillage Res. 221. https://doi.org/10.1016/j.still.2022.105414.

Otieno, E.O., Kiboi, M.N., Gian, N., Muriuki, A., Musafiri, C.M., Ngetich, F.K., 2021. Uptake of integrated soil fertility management technologies in heterogeneous smallholder farms in sub-humid tropics. Environ. Chall. 5, 100394. https://doi.org/10.1016/j.envc.2021.100394.

Peel, M.C., Finlayson, B.L., McMahon, T.A., 2007. Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci. 11, 1633-1644. https://doi.org/10.5194/hess-11-1633-2007.

Penot, E., Chambon, B., Myint, T., 2021. Economic calculations for assessing agricultural systems Cost benefit analysis and farm level real budget analysis. Agric. Res. Dev. 1-25.

Pettigrew, W.T., Meredith, W.R., Bruns, H.A., Stetina, S.R., 2006. Effects of a short-term corn rotation on cotton dry matter partitioning, lint yield, and fiber quality production. J. Cotton Sci. 10, 244-251.

Pöhlitz, J., Rücknagel, J., Schlüter, S., Vogel, H.J., Christen, O., 2019. Computed tomography as an extension of classical methods in the analysis of soil compaction, exemplified on samples from two tillage treatments and at two moisture tensions. Geoderma 346, 52-62. https://doi.org/10.1016/j.geoderma.2019.03.023.

Potratz, D.J., Mourtzinis, S., Gaska, J., Lauer, J., Arriaga, F.J., Conley, S.P., 2020. Strip-till, other management strategies, and their interactive effects on corn grain and soybean seed yield. Agron. J. 112, 72-80. https://doi.org/10.1002/agj2.20067.

Rahman, M.M., Alam, M.S., Kamal, M.Z.U., Rahman, G.K.M.M., 2020. Organic Sources and Tillage Practices for Soil Management. In: Kumar, S., Meena, R.S., Jhariya, M.K. (Eds.), Resources Use Efficiency in Agriculture. Springer, Singapore. https://doi.org/10.1007/978-981-15-6953-1-9.

Reddy, S.S., Nyakatawa, E.Z., Reddy, C.K., 2012. Nitrogen uptake pattern by cotton in a long-term no-tillage system with poultry litter application. Int. J. Agric. Biol. 14, 29-37.

Ripoche, A., Crétenet, M., Corbeels, M., Affholder, F., Naudin, K., Sissoko, F., Douzet, J. M., Tittonell, P., 2015. Cotton as an entry point for soil fertility maintenance and food crop productivity in savannah agroecosystems-Evidence from a long-term experiment in southern Mali. Field Crops Res. 177, 37-48. https://doi.org/10.1016/j.fcr.2015.02.013.

Saito, K., Azoma, K., Oikeh, S.O., 2010. Combined effects of Stylosanthes guianensis fallow and tillage management on upland rice yield, weeds and soils in southern Benin. Soil Tillage Res. 107, 57-63. https://doi.org/10.1016/j.still.2010.03.001.

Sime, G., Aune, J.B., Mohammed, H., 2015. Agronomic and economic response of tillage and water conservation management in maize, central rift valley in Ethiopia. Soil Tillage Res. 148, 20-30. https://doi.org/10.1016/j.still.2014.12.001.

Singh, J., Wang, T., Kumar, S., Xu, Z., Sexton, P., Davis, J., Bly, A., 2021. Crop yield and economics of cropping systems involving different rotations, tillage, and cover crops. J. Soil Water Conserv. 76, 340-348. https://doi.org/10.2489/jswc.2021.00117.

Sinha, N.K., Mohanty, M., Jayaraman, S., Kumar, J., Kumar, D., Rani, A., 2021. Implication of different tillage system on root system architecture and their environment. In: Jayaraman, I.n.S., Dalal, R.C., Patra, A.K., Chaudhari, S.K. (Eds.), Conservation Agriculture: A Sustainable Approach for Soil Health and Food Security. Springer Singapore, pp. 451-475. https://doi.org/10.1007/978-981-16-0827-8-23.

Sommer, R., Thierfelder, C., Tittonell, P., Hove, L., Mureithi, J., Mkomwa, S., 2014. Fertilizer use should not be a fourth principle to define conservation agriculture. Response to the opinion paper of Vanlaume et al. (2014) 'A fourth principle is required to define conservation agriculture in sub-Saharan Africa: The appropriate use of fe. Field Crops Res. 169, 145-148. https://doi.org/10.1016/j.fcr.2014.05.012.

Soumaré, M., Havard, M., Bachelier, B., 2021. Cotton in West and Central Africa: from the agricultural revolution to the agro-ecological transition. Cah. Agric. 30. 5. https://doi.org/10.1051/cagri/2020044.

R.Core Team, 2021. R: A Language and Environment for Statistical Computing.

Teixeira, H.M., Schulte, R.P.O., Anten, N.P.R., Bosco, L.C., Baartman, J.E.M., Moinet, G. Y.K., Reidsma, P., 2022. How to quantify the impacts of diversification on sustainability? A review of indicators in coffee systems. Agron. Sustain. Dev. 42. https://doi.org/10.1007/s13593-022-00785-5.

Temesgen, M., Savenije, H.H.G., Rockström, J., Hoogmoed, W.B., 2012. Assessment of strip tillage systems for maize production in semi-arid Ethiopia: effects on grain yield, water balance and water productivity. Phys. Chem. Earth Parts A/B/C 47-48, 156-165. https://doi.org/10.1016/j.pce.2011.07.046.

Thierfelder, C., Matemba-Mutasa, R., Rusinamhodzi, L., 2015. Yield response of maize (Zea mays L.) to conservation agriculture cropping system in Southern Africa. Soil Tillage Res. 146, 230-242. https://doi.org/10.1016/j.still.2014.10.015.

Thierfelder, C., Matemba-Mutasa, R., Bunderson, W.T., Mutenje, M., Nyagumbo, I., Mupangwa, W., 2016. Evaluating manual conservation agriculture systems in southern Africa. Agric. Ecosyst. Environ. 222, 112-124. https://doi.org/10.1016/j.agee.2016.02.009.

Thierfelder, C., Mhlanga, B., 2022. Short-term yield gains or long-term sustainability? - a synthesis of Conservation Agriculture long-term experiments in Southern Africa. Agric. Ecosyst. Environ. 326. https://doi.org/10.1016/j.agee.2021.107812.

Thierfelder, C., Mwila, M., Rusinamhodzi, L., 2013. Conservation agriculture in eastern and southern provinces of Zambia: long-term effects on soil quality and maize productivity. Soil Tillage Res. 126, 246-258. https://doi.org/10.1016/j.still.2012.09.002.

Tittonell, P., Scelop, E., Andrieu, N., Posthumus, H., Mapfumo, P., Corbeels, M., van Halsema, G.E., Lahmar, R., Lugandu, S., Rakotoarisoa, J., Mtambanengwe, F., Pound, B., Chikowo, R., Naudin, K., Triomphe, F., Mkomwa, S., 2012. Agroecology-based aggradation-conservation agriculture (ABACA): targeting innovations to combat soil degradation and food insecurity in semi-arid Africa. Field Crops Res. 132, 168-174. https://doi.org/10.1016/j.fcr.2011.12.011.

Topa, D., Cara, I.G., Jitäreanu, G., 2021. Long term impact of different tillage systems on carbon pools and stocks, soil bulk density, aggregation and nutrients: A field meta-analysis. CATENA 199, 105102. https://doi.org/10.1016/j.CATENA.2020.105102.

Townsend, T.J., Ramsden, S.J., Wilson, P., 2016. How do we cultivate in England? Tillage practices in crop production systems. Soil Use Manag. 32, 106-117. https://doi.org/10.1111/sum.12241.

van Balen, D., Cuperus, F., Haagsma, W., de Haan, J., van den Berg, W., Sukkel, W., 2023. Crop yield response to long-term reduced tillage in a conventional and organic farming system on a sandy loam soil. Soil Tillage Res. 225, 105553. https://doi.org/10.1016/j.still.2022.105553.

Vetsch, J.A., Randall, G.W., Lamb, J.A., 2007. Corn and soybean production as affected by tillage systems. Agron. J. 99, 952-959. https://doi.org/10.2134/agronj2006.0149.

Yemadje, P.L., Takpa, O., Amonide, I., Balarabe, O., Sekloka, E., Guibert, H., Tittonell, P., 2022. Limited yield penalties in an early transition to conservation agriculture in cotton-based cropping systems of Benin. Front. Sustain. Food Syst. 6. https://doi.org/10.3389/fsufs.2022.1041399.

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

Youssouf, I., Lawani, M., 2000. Les sols béninois: classification dans la Base de référence mondiale. Quatorzième Réunion Du Sous-Comité Ouest et Centre Africain de Corrélation Des Sols Pour. La Mise En. Valeur Des Terres.

Zhou, X.V., Larson, J.A., Sykes, V.R., Ashworth, A.J., Allen, F.L., 2021. Crop rotation, cover crop, and poultry litter effects on no-tillage cotton profitability. Agron. J. 113, 2648-2663. https://doi.org/10.1002/agj2.20661.