1. Introduction

Chickpea (Cicer arietinum L.) is a vitallegume crop cultivated primarily by smallholder farmers in arid and semi-arid regions globally, covering approximately 13.7 million hectares (Rezapour et al., 2021; Thudi et al., 2021; Food and Agriculture Organization, 2024). It plays a crucial role in human nutrition, providing a healthy food source in both developed and developing countries (Merga, Haji, 2019). In Iran, chickpeas, with an average yield of 409 kg ha^-1, occupy the largest cultivated area among legumes (Food and Agriculture Organization, 2024). Understanding the factors that reduce yield is essential for increasing productivity. Among these factors, weeds are considered the most significant threat to chickpea yields (Rajput et al., 1986; Oerke, 2006).

Generally, legumes like chickpeas exhibit poor competitive ability against weeds (Yung et al., 2015) due to their slow establishment, limited canopy closure, and reduced height and leaf area (Mohammadi et al., 2005). In Iran, 14 weed species have been identified as predominant in chickpea fields, causing issues not only during the growing season but also complicating harvesting operations (Nosratti et al., 2020). Key weeds in the western regions of Iran include Wild safflower (Carthamus oxyacantha M. Bieb.) and chicory (Cichorium intybus L.), field bindweed (Convolvulus arvensis L.), hare's ear mustard [Conringia orientalis (L.) Dumort], catchweed (Galium aparine L.), wild mustard (Sinapis arvensis L.), cowcockle (Vaccaria pyramidata), wild oat (Avena fatua L.), bermudagrass [Cynodon dactylon (L.) pers.] and licorice (Glycyrrhiza glabra L.) (Nosratti et al., 2020).

Recent studies indicate that while weed diversity can offer advantages, it also presents certain challenges. An increase in weed diversity may lead to the emergence of highly competitive and problematic species, which necessitates careful monitoring for effective weed management (Ferrero et al., 2017; Adeux et al., 2019). Despite these concerns, a positive relationship between weed diversity and crop productivity has been observed. More diverse weed communities can enhance crop performance by increasing weed-weed interference (Cierjacks et al., 2016; Ferrero et al., 2017; Adeux et al., 2019; Gonzalez-Andujar et al., 2019), which may reduce the prevalence of dominant weed species through interspecific competition, thereby reducing their interference with crops (Cléments et al., 1994; Hooper et al., 2005; Adeux et al., 2019). Furthermore, weed diversity supports agro-ecosystem functions such as nutrient cycling (Hacker et al., 2015), pollination (Nel et al., 2017), and providing habitats for beneficial insects and biological control agents (Capinera, 2005; Barberi et al., 2010).

Weed-crop interference has primarily been studied through experimental designs focusing on a limited number of weed species (Guglielmini et al., 2017). Under field conditions, crop yields are often influenced by multi-species interactions yet little is known about the effects of complex weed communities (Adeux et al., 2019). Investigating multi-species interference at the field scale and assessing both intra- and inter-species competition can provide valuable insights for effective weed management. For instance, It have found that two-species interference between Amaranthus hyhridus and Sorghum halepense had a greater competitive effect on soybean than single-species interference (Toler et al., 1996). Similarly, it was demonstrated that simultaneous competition from two weed species resulted in greater yield reductions compared to individual species competition (Sims, Oliver, 1990).

The growth type and lifespan of different weed species vary significantly; thus, effective weed management programs must align with the biological traits of weeds present in the fields (Kayan, Adak, 2006). Not all weed species negatively impact crop yield—some may even provide benefits. Increasing weed diversity can reduce competitive pressure on crops and mitigate yield losses (Martin-Fores et al., 2017; Adeux et al., 2019). Understanding these complex relationships between weed communities and crops is crucial for informing effective management decisions. Therefore, the objectives of the present study were: a) identify naturally assembled weed communities in chickpea fields within the study area, b) evaluate the effect of weed infestation on chickpea yield based on density or canopy cover data, c) assess multi-species interference among weeds in chickpea, d) explore the relationship between weed biodiversity and chickpea yield.

2. Materials and Methods

2.1 Study area

This study was conducted in 2015 in the Sanjabi rural district (Latitude: N 34º 43', Longitude: E 46º 40', 1,320 m above sea level) located in the northwest of Kermanshah Province, Iran. The annual mean temperature of the study area is 16.8ºC and the long-term average rainfall is about 521 mm, usually occurring from late-October to early-May with no precipitation during the late spring and whole of the summer. Environmental variables of the study area are represented in Table 1. The study area included the three adjacent villages with the same soil and weather conditions, as well as agronomic management equipment and activities (on the basis of an interview with the farmers). Agronomic management (for example seedbed preparation, sowing method, source of seed supply, type of chickpea cultivar, seeding rate, and weed control) had been implemented in a similar way for at least the last five years. Furthermore, we asked farmers about their last 5 year field cropping history. There were two types of crop rotation in the study area included rotations with two (wheat-chickpea) and three (wheat-chickpea-barley-chickpea) annual crops continuously cultivated across consecutive years. Among the studied fields, 67 had two crops and 18 had three crops in rotation. Therefore, the weed composition and diversity were analyzed between the two rotations to ensure there are no rotation-weed composition interactions. Accordingly, factor analysis was used to evaluate weed composition in the two rotations. Furthermore, the independent t-test by assuming equal variance was applied to compare weed diversity and evenness between the two rotations. In order to perform these analyzes, we used weed density data.

The climatic conditions of the study area were almost the same in the year of the study as the previous year. For example, the average rain or snow precipitation and average annual temperature were 348.44 mm and 16.8ºC in 2015 and 348.95 mm and 15.8ºC in 2014 (Meteorological data). Regarding, the climatic similarity of 2014 and 2015 and considering the dependence of spatial dynamics of the weed population on spatial factors, we tried to make the size of the sampling area as large as possible to cover different weed abundance and diversity variations in a one-year study. The study area has 329 rainfed fields with an average size of 9.5 hectares, cultivated with chickpea, lentils, wheat, and barley. Chickpea was cultivated as an annual crop in 108 of the fields, therefore 85 fields were selected for sampling based on the sample size table (Krejcie Morgan, 1970).

2.2 Sampling

In order to avoid field edges effects on the weed population, approximately one hectare was selected from the center of each field. Sampling was carried out based on stratified random sampling (Nkoa et al., 2015). In this method, an initial visual estimated of the weed density was made by a walking in the fields. Based on observations, weed spatial distribution of the investigated fields was patchy and a large variation of weed densities was observed. Thus, weed infestation was divided into three strata (low, moderate, and severe weed infestation; less than 20%, 20% to 40%, and more than 40% of weed canopy cover, respectively). Each stratum was randomly sampled using two 1 m^-2 quadrats; thus, six sampling points were selected in each field and a total of 510 points were sampled in the study area. Weed species density (plants m^-2), and canopy cover proportion were recorded at the chickpea phenological stages of a) four to seven-leaf and b) mid to early-podding in the same sampling points. At the first sampling stage, wooden stakes were used to mark the sampling points as well as, a handheld GPS receiver (Garmin eTrex Summit) was used to record the spatial position of the sampling points and identify the points at the next sampling stages (Chickpea yield was recorded simultaneously with chickpea maturity stage in the same sampling points of the first and second sampling stages). Weed species identification was made with the assistance of weed specialists and botanists in the herbarium of Razi University. Weed density was calculated as the total number of a particular weed species m^-2. The canopy cover proportion was estimated visually using a reticulated quadrat (Brim-DeForest et al., 2017).

Weed species richness (the number of weed species per quadrat), biodiversity index of Shannon-Weiner (Equation 1), as well as, evenness index of Camargo (Equation 2) were calculated using weed species density data obtained from the first and second sampling stages.

Here H' is the Shannon-Weiner diversity Index, pi is the proportion of species i relative to the total number of species which defined as pi = ni/N (ni is the number of species i and N are the total number of species) and s is the total number of species (Shannon, Weaver, 1964).

In this equation, E' is the Camargo's evenness index, pi is the proportion of species i in the sample, pj is the proportion of species j in the sample, and s is the total number of species (Camargo, 1993).

2.3 Data analysis

In this study, 510 plots were sampled with the weed density and canopy cover in these plots varying in the range of 0 to 50 plants m^-2, and 0 to 55% respectively. Therefore, the regression was used to investigate the effects of the weed traits (weeds density and canopy cover, as well as weed evenness and diversity) as independent variables on chickpea yield. The statistical normal distribution of the chickpea yield as the dependent variable was determined by the Kolmogorov Smirnov normality test, which indicated a normal distribution. Stepwise multiple regression analysis was used to evaluate the combined effect of independent variables (weed density and canopy cover as well as weed diversity and evenness at mentioned sampling stages) on chickpea yield as the dependent variable. Thus, the most effective factors were identified in each sampling stage. The polynomial regression was fitted to achieve the best approximation of the chickpea yield to each significant output factors of stepwise regression (weeds density and weed population evenness at the four to seven-leaf stage of chickpea and weed canopy cover and weed population diversity at the early-podding stage of chickpea). Furthermore, the interactions among individual weed species density and canopy cover, and chickpea yield were investigated by the Spearman correlation method. The statistical distribution of weed species density and canopy cover was not normal (no way was found to data transformation based on Johnson transformation method), So the Spearman nonparametric method was used to assess correlation between individual weed species and chickpea yield. Principal component analysis (PCA) was implemented using SPSS V.20 to investigate the relationships among weed evenness and diversity, chickpea yield, as well as predominant weed composition of studied fields.

3. Results and Discussion

3.1 Weed species composition

Weed composition within the rotations was almost the same based on the output of factor analysis (Figure 1). Furthermore, no differences of weed diversity and evenness between wheat-chickpea, and wheat-chickpea-barley-chickpea rotations were identified. There were no rotationweed composition interactions.

In the first and second stages of sampling, 27 weed species were identified in the studied fields (Table 2). The most frequent weeds were C. intyhus, C. oxyacantha, C. arvensis, C. orientalis, G. aparine, S. arvensis, G. glabra, mouse barley (Hordeum murinum L.), and Syrian cephalaria [Cephalaria syriaca (L.) Roem & Schult], which accounted for almost 93% of the total weed density in both the first and second sampling stages. In the first sampling stage, annual and perennial weeds accounted for 45% and 55% of the total weeds, respectively. In the second sampling stages, these values were 42% and 58%. Among perennial weeds species C. intyhus, and C. arvensis accounted for 50% and 54% of the weed frequency at the first and second sampling stages, respectively. Among the annual weeds, C. oxyacantha had the greatest density. The most frequent families in the two sampling stages were Asteraceae and Brassicacae, which accounted for 55% and 13% of the total weed families in the first sampling stage and 58% and 11% in the second sampling stage. In general, 95% of total family frequency in both sampling stages were broadleaf weeds. While, Poaceae accounted for 5% of total weed families' frequency at both the first and second sampling stages, and the dominant grass weed species was H. murinum (Table 2).

An experiment in Ethiopia showed that broadleaf weeds accounted for more than 93% of the chickpea weed flora (Merga, Alemu, 2019). In this study, Asteraceae had the greatest relative density among weed families, in a study conducted in India showed that major weed flora in chickpea included broadleaf weeds. However, the composition of broadleaf weeds in chickpea varied among different regions (Nath et al., 2018). Merga and Alemu (2019) categorized Solanum nigrum L., Medicago polymorpha L., Galinsoga ciliate, and Commelina henghalensis L. as high density species in their study. While in the study of Nath et al. (2018)Sonchus arvensis L., Chenopodium album L., Euphorbia geniculate L., Vicia hirsuta (L.) Gray, Physalis minima L., Rumex dentatus L., Medicago denticulata L., and Cirsium arvense (L.) Scop, scored ****highest relative weed density. Chalechale et al. (2015) studied the weed population in the chickpea field of the Kermanshah and recorded 61 weed species which broadleaf weeds were most abundant. In the current study, the dominant broadleaf weeds included C. intybus, C. arvensis, G. tricornutum, C. oxyacantha, and V. pyramidata and the dominant grass weeds consisted of H. spontarcum, A. fatua, C. dactylon.Mousavi et al. (2010) reported that Acroptilon repens (L.) DC, C. intybus, C. oxyacantha, G. glabra, Lactuca serriola L., S. arvensis, and Sophora alopecuroides L. are common weed species of chickpea fields in Kermanshah. The high abundance of broadleaf weeds indicates that these species are a great menace to chickpea production in the study area and proper control of such weeds should be considered in weed management programs. Understanding weed population composition is vital for effective weed management as it helps identify dominant species, tailor control strategies, and predict responses to agricultural practices. This knowledge enables farmers to implement integrated weed management (IWM) approaches, enhancing crop yields while minimizing environmental impacts (Dorner et al., 2024).

3.2 The effect of weed density and canopy cover on chickpea yield

In the first sampling stage, chickpea yield was influenced (P-value < 0.01) by weed density, while weed canopy cover did not show a effect. Specifically, as weed density increased from 0 to 50 plants rrr², chickpea yield decreased from 330 kg ha^-1 to 209 kg ha^-1 (Figure 2A). This decline in yield is likely due to increased interspecific competition among weed species at higher densities, which restricts the resources available to the chickpeas.

During the second sampling stage, however, weed canopy cover had a negative impact {P-value < 0.01) on chickpea yield. No significant interaction between chickpea yield and weed density was observed. The analysis indicated that chickpea yield dropped from 327 kg ha"¹ in plots with no weed cover to 190 kg ha"¹ in plots with 55% weed canopy cover (Figure 2B). Additionally, the mean canopy cover of weeds increased from 4.14% during the first sampling stage to 16.06% in the second stage. This increase in canopy cover can be viewed as an indicator of increased weed biomass, which corresponds to heightened competitive pressure on the chickpea crop.

The critical period for effective weed control in chickpeas is identified as being between the four-leaf stage and just before flowering (Mohammadi et al., 2005). During this early growth phase, weeds can exert a significant influence on chickpea yield. Previous research has established a strong negative correlation between weed dry weight and chickpea grain yield (Al-Thahabi et al., 1994). The slow establishment of chickpea seedlings, combined with limited vegetative growth and canopy expansion during these initial stages, makes them particularly vulnerable to competition of weeds (Miller et al., 2002). As a result, unchecked weed competition can substantially decrease both chickpea yield and its components (Saxena et al., 1996). Therefore, neglecting weed control during this critical management period can lead to considerable reductions in chickpea yield.

In the second sampling stage, the impact of weed canopy cover on chickpea grain yield was pronounced. In agricultural settings, plant species compete for solar radiation, enhancing their access to light while shading neighboring plants (Tardy et al., 2015). In chickpea fields, limited canopy expansion means that competition for light between the crop and weeds is unavoidable. This competition is largely determined by factors such as plant height and canopy cover (Siebert, Stewart, 2006; Duchene et al., 2017). Increased competition for light not only reduces the quantity of light received by each plant but can also affect light quality (Taiz et al., 2018). These two factors can ultimately lead to diminished photosynthesis and reduced dry matter production in plants (Rao et al., 1991; Tardy et al., 2015). Consequently, an increase in weed canopy cover during the podding phenological stage of chickpeas appears to negatively impact photosynthesis and grain filling, further contributing to reduced yields.

3.3 The interactions of weed species and chickpea yield

In the first stage of sampling, chickpea yield was negatively correlated with the density of the most dominant weeds. Specifically, C. intyhus and C. oxyacantha exhibited significant negative correlations with chickpea yield (Table 3). These two species were the most frequently observed weeds in the studied fields (Table 2), reinforcing their importance in affecting chickpea yield. Other species, such as C. orientalis, G. aparine, Sinapis arvensis, and C. syriaca, also showed negative correlations with chickpea yield, though these were not statistically significant. Conversely, a positive correlation was noted between G. glabra and H. spontaneum with chickpea yield, although these relationships were not significant (Table 3).

In the second stage of sampling, the investigation into weed canopy cover revealed that C. intybus and C. oxyacantha maintained their negative and significant correlations with chickpea yield. Additionally, G. glabra, H. murinium, and H. spontaneum had positive correlations with chickpea yield, yet these were also not significant (Table 4). Although the positive correlation can be overlooked statistically, it emphasizes the complexity of plant interactions within agricultural ecosystems, where certain weed species may provide benefits despite competitive pressures.

The germination of C. intybus coincides with that of chickpeas in western Iran, allowing it to thrive in low-input agricultural systems where minimal fertilization occurs (Chalechale et al., 2015). This species' ability to grow effectively in dryland conditions, coupled with its strong root development which enables it to access water and nutrients (Tkach et al., 2022) contributes to its prevalence as a problematic weed in chickpea fields. Similarly, C. oxyacantha has a deep and extensive root system that allows it to penetrate deeper soil layers, accessing moisture and nutrients. This capability is crucial in arid and semi-arid environments where water scarcity is a significant challenge for crop production (Aslam et al., 2024). C. oxyacantha poses challenges not only through competition but also by complicating harvesting operations due to its spiny nature (Nosratti et al., 2020). Its rapid vegetative growth allows it to complete its life cycle before water stress periods, further establishing it as a weed in these regions.

Interestingly, while many weed species negatively correlated with chickpea yield, some, like G. glabra, showed positive associations. This suggests potential complementary interactions within plant communities where mutualistic effects may outweigh competitive ones Sometimes the impact of complementarity is stronger than the competition, in which case the negative effects of competition are compensated or eliminated and the overall use of resources will be improved through the positive interactions (Bedoussac et al., 2015). For instance, legumes can enhance soil phosphorus availability through root acidification and increased mycorrhizal fungi diversity (Hinsinger et al., 2011; Duchene et al, 2017; Weisser et al, 2017).

The concept of indirect facilitation indicates that competition among weeds can alter the competitive dynamics for chickpeas. For example, the interspecific competition between G. glabra and dominant weeds may reduce the negative impact of those weeds on chickpeas (Rooker et al., 2008). Although studies have shown that certain weeds can negatively affect crop yields when competing individually or in population (Toler et al., 1996), the presence of beneficial species like G. glabra may mitigate these effects through allelopathic interactions or resource sharing (Mohammadi et al., 2004; Mustafa et al., 2018). Understanding these complex relationships among weed species is crucial for developing effective weed management strategies that not only focus on controlling dominant weeds but also leverage beneficial interactions to improve productivity.

The interactions between weeds and crops are significantly affected by soil characteristics, climate conditions, and local agricultural practices. Soil properties influence weed emergence, growth, and competition, with nutrient availability often fluctuating due to changes in soil pH (Barber, 1981). Specific nutrients such as magnesium, sulfur, copper, manganese, and zinc can notably affect the composition of weed species (Dorner et al., 2024). Additionally, soil texture is crucial for determining soil fertility and the types of weed species that impact crop performance (Tóth et al., 2008). Climate variables, including temperature (Ramesh et al., 2017) precipitation patterns, and atmospheric C02 levels (Vilà et al., 2021), also play a vital role in shaping weed-crop interactions, making it essential to understand their effects on crop competitiveness. Agricultural practices are pivotal in influencing these interactions. Factors such as sowing methods and rates (Bagheri et al., 2021), fertilization strategies (Amaral et al., 2018), and various weed control methods (Khan et al., 2023) contribute to the impact of weeds on chickpea production. Integrating environmental factors with agricultural practices through IWM provides a sustainable approach to managing weeds in chickpea systems. By understanding the interplay between climate conditions, soil traits, and diverse agricultural activities, farmers can develop effective strategies that enhance crop yields while promoting ecological balance and sustainability in agriculture.

3.4 Effect of weed biodiversity on chickpea yield

The study found that both the evenness and diversity of weed populations positively affected (P-value ≤ 0.01) chickpea yield during the first and second stages of sampling, respectively. Specifically, chickpea yield increased from 241 kg ha^-1 to 307 kg ha^-1 as the evenness index rose from 0.261 to 0.791 (Figure 3A). Similarly, yield increased from 219 kg ha^-1 at a weed diversity index of 1.255 to 339 kg ha^-1 at a diversity index of 2.92 (Figure 3B).

PCA was conducted to further investigate the relationships between prominent weed species, weed diversity, evenness, and chickpea yield (Figure 4). The results of Bartlett's test of sphericity indicated that the PCA was appropriate for the data (P-value = 0.00). The PCA ordination diagram revealed significant associations between weed diversity, evenness, and chickpea yield. Notably, G. glabra was distinctly separated from other weeds and showed a strong association with both weed evenness and chickpea yield. In contrast, C. intyhus and C. oxyacantha were positioned farthest from chickpea yield in the PCA diagram, indicating weak associations with both weed diversity and evenness.

The positive correlation between weed evenness, diversity, and chickpea yield aligns with findings from other studies examining the impact of these factors on crop performance (Cierjacks et al., 2016; Adeux et al., 2019). Weed evenness reflects the distribution of species frequencies within plant communities (Mason et al., 2005), suggesting that higher levels of evenness and diversity facilitate a more equitable sharing of resources among plant species within a given niche (Ejtehadi et al., 2009). This coexistence is supported by spatio-temporal heterogeneity in resource availability, which reduces competition for crops and limits the dominance of competitive weed species through increased inter-species interference (Storkey, Neve, 2018; Adeux et al, 2019). In this study, the dominant weed species—C. intyhus and C. oxyacantha—exhibited the least association with weed evenness and diversity, correlating with lower chickpea yields. The results indicate that in areas where weed diversity and evenness were high, there was a corresponding decrease in dominant weed species, which had a negative correlation with chickpea yield. A diverse plant community can foster complementary or facilitative interactions among species (Weisser et al., 2017). Previous research has shown that greater weed diversity is positively associated with crop yields in various crops, including maize and soybeans (Ferrero et al., 2017), as well as in coconut and banana crops (Cierjacks et al., 2016). Additionally, (Yang et al., 2019) reported significant positive effects of weed species diversity on aboveground biomass due to complementarity effects. These findings underscore the importance of managing weed communities to enhance crop productivity through improved biodiversity.

4. Conclusions

This study elucidates the complex interactions between weeds and chickpea productivity, emphasizing that while the presence of weeds generally hinders chickpea yield due to increased competition for resources, there are instances where weed diversity can confer benefits. Specifically, the findings indicate that higher weed density and canopy cover negatively impact chickpea yields. However, a positive correlation between weed diversity and evenness with chickpea yield suggests that diverse weed communities may enhance resource sharing and reduce competition from dominant species.

These results underscore the importance of understanding the dual nature of weed-crop interactions for effective management strategies. It is essential to prioritize not only the control of dominant competitive weeds but also the promotion of beneficial weed diversity to support crop production and environmental sustainability. Weed management programs should focus on promoting weed diversity while carefully controlling the presence of dominant competitive species. Although reducing dominant species may lead to the emergence of other dominant weeds, encouraging a diverse weed community can help establish a more balanced ecosystem that lessens the adverse effects of any one species. Proactively managing weed diversity by recognizing the potential benefits of multi-species relationships can enhance crop productivity and sustainability at various scales—from fields to entire landscapes. Future research should explore the effects of weed-crop interactions to design cropping systems that leverage positive relationships, aiming for environmentally friendly and sustainable agricultural practices. Investigations should also delve into specific interactions within plant communities to clarify positive or negative effects, ultimately balancing effective weed control with biodiversity preservation.

References

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