Abstract
These two-year experiments determined the best predictors of bean growth and fly, root rots, and weed development in different cultivars, planting dates and weed treatments across 256 plots. Root rot diseases were naturally caused by Fusarium solani and Rhizoctonia solani. Weed management treatments involved: herbicide (Imazethapyr or Trifluralin) application, hand-weeding and control. Parameters estimated by exponential, Gaussian and linear-by-linear models fitted to bean, disease and weed datasets were considered as progress curve elements. Factor analysis detected the most predictive variables to characterize bean growth and production, disease, fly and weed development over season. There were significant correlations between considered plant, disease, pest and weed descriptors. Based on principal component analysis, considered bean-disease-fly-productivity-weed predictors justified 70% of total variance in datasets. Finally, multivariate regression model involving eight selected predictors explained a noticeable part (63%) of yield variations. Such information may improve accuracy of future efforts to monitor bean, disease, fly and weed development, predict bean yield and develop integrative field management programs.
Keywords: Legumes, Multiple regression, Soil-borne pathogens
Legumes; Multiple regression; Soil-borne pathogens.
1. Introduction
Fly, root rots and weed are considered as major damaging agents in bean cultivation worldwide (Naseri 2019b; Oveisi et al., 2021). A systematic understanding of the interaction of bean production with the agrosystems, root rot diseases, fly and weed may lead to a more concise prediction of bean production and subsequently the development of integrated crop management programs. A number of bean fly, root rots and weed management methods including bean resistance and agronomic practices (Naseri 2019a; Sariah and Makundi 2007), biological and chemical (Naseri and Younesi 2021; Tabande and Naseri 2020) strategies have been documented previously. Trutmaun and Graf (1993) modeled bean production based on survey datasets of diseases and pests. Naseri (2014) described bean production using Fusarium root rot and soil predictors described on a regional basis in Iran. However, the associations of bean production to the over-season development of bean fly, root rot diseases and weed across different bean cultivars grown under various planting dates and weed control treatments deserve much more investigation.
Bean fly (Ophiomyia sp.) is considered the most predominant and destructive insect pest in Iranian (Naseri, 2019a) and eastern African beans (Greathead, 1968). Yield reductions as high as 100% have been reported in fields heavily infested by bean fly (Talekar, 1992). The spread and damage due to bean fly are dependent on the growing region and season (Naseri, 2019b; Ojwang et al., 2010). For instance, the drought stress in the lower semi-arid areas of east Africa has intensified bean fly infestations. In Kenya (Ojwang et al., 2010), the susceptibility of different bean lines and cultivars to bean fly was explored under various environmental conditions. Furthermore, Ojwang et al. (2010) reported 3–69% seed yield reduction as a result of bean fly attacks. Thus, attempts have been made to develop sustainable pest management methods. Van der Goot (1930) found increased mortality in beans attacked by fly in plantings delayed by three weeks. Likewise, Sariah and Makundi (2007) demonstrated a greater fly infestation in late planted beans in northern Tanzania.
Besides yield losses to this pest, Fusarium and Rhizoctonia root rots caused by Fusarium solani f. sp. phaseoli and Rhizoctonia solani, respectively, have been much considered as major damaging diseases in bean cropping systems around the world (Dehghani et al., 2018; Naseri and Younesi, 2021). In Iran, severe root rot diseases and low bean productivity corresponded with the lack of herbicide application, deep and early planting, and dense weed populations in commercial fields (Naseri and Marefat, 2011; Naseri and Moradi, 2015). However, an advanced understanding of joint associations of bean fly and root rot diseases with productivity under different agricultural practices at field plot scale is still needed to minimize the crop damage.
In addition to bean fly and root rot diseases, weeds are still major threats to the crop production worldwide. Although herbicides can efficiently decrease crop losses to weeds, the chemical weed control must increase agricultural expenses, weed resistance to herbicide, environmental instability and human health risks (Oveisi et al., 2021). Thus, it is required to reduce herbicide usage as much as possible with the assistance of more efficient agronomic practices for sustainable bean production. For instance, Oveisi et al. (2021) found improved bean competitiveness and herbicide efficiency following the joint application of herbicides and the mixed-cropping. Kakhki et al. (2022) added that late sowings of beans in late spring not only reduced Fusarium root rot, herbicides usage and weed threats, but also improved 100-seed weight and yield. Furthermore, they modeled the over-season variability in plant growth, Fusarium root rot and weeds in order to characterize bean, disease and weed development at field-plot scale. Moreover, their findings supported previous plot- and large-scale findings on restricted root rot diseases and improved seed production in late (early summer) sown bean crops in Iran (Naseri, 2019a, Naseri, 2019b; Naseri and Marefat, 2011; Naseri and Mousavi, 2013). To further extend the advantage of such environmental-friendly strategies in the integrated crop management, attempts were made to examine: (1) the interrelationships among 27 indicators described for bean growth, fly, Fusarium and Rhizoctonia root rots, weed, pod and seed production, and yield in two commercial cultivars differing in planting date and weed management treatments during two growing seasons; and (2) the descriptive value of bean-disease-fly-weed indicators to predict bean productivity and yield.
2. Materials and methods
2.1. Experimental design
In 2014 and 2015 seasons, bean production, fly infestation, Fusarium and Rhizoctonia root rots, and weed were evaluated across 256 experimental plots as presented in Table 1. Natural infestations of plots with fly, F. solani f. sp. phaseoli, R. solani and weed populations were confirmed by the pre-test of soil (clay-loamy, mixed, mesic, Typic Haploxerepts) samples collected from the plots. There were no significant differences in the levels of F. solani f. sp. phaseoli, R. solani and weed populations determined for each experimental plot. The experimental plots were prepared at Kheirabad Research Station (latitude 36°31′N, longitude 48°47′E; 1,770 m a.s.l; average 285 mm rainfall and 142 frost days per year). The experiments were conducted using a design of split-split-plot with four replications (Table 1).
Table 1.
Experimental plots characteristics, factor levels and assessments.
| Assessments | Plot no. | Experimental factors |
||
|---|---|---|---|---|
| Planting date (Main plot) | Weed control (Subplot) | Cultivar (Sub-subplot) | ||
| Bean biomass | 128 in | 10–15 May | Imazethapyr usea | COS16 |
| Bean leaf area | 2014 | (Mid-spring in Iran) | Trifluralin useb | (Bush bean) |
| Fusarium root rot | 128 in | 26–31 May | Hand-weeding | Talash |
| Rhizoctonia root rot | 2015 | 10–15 June | Control | (Climbing |
| Weed biomass | 25–30 June | bean) | ||
| Weed density | (Early summer in Iran) | |||
| Bean height | ||||
| Maximum fly | ||||
| Pod no./Plant | ||||
| Seed no./Pod | ||||
| Yield (kg/ha) | ||||
Plot characteristics.
Consisted of six 5 m rows spaced by 0.30 m, plant spacing of 0.075 m.
Ploughed & Fertilized with 100 kg/ha urea.
Previous crop in 2014, sunflower, and in 2015, wheat.
Pre-emergence use of Imazethapyr (PursuitTM 10% SL) at 1 l/ha.
Pre-planting Trifluralin (TreflanTM 48% EC) at 2.5 l/ha.
To rate fly attacks, five bean plants per quadrat (0.6 m × 0.6 m; one quadrat per plot) were sampled randomly on a weekly basis from seedling stage to harvest to be assessed in the laboratory. The percentage of plants being attacked by flies was determined for every plot. The leaf area (cm2/m2) was assessed using an automatic leaf area meter (model LI-3100, LI-COR, Lincoln, NE, USA) for detached leaves of bean plants within one quadrat per plot at seedling once with one leaflet and next with three leaflets fully opened, 50% flowering, podding and maturity stages. Weed biomass was detected as the weight of dry matter for weed plants within each quadrat (0.6 m × 0.6 m; one quadrat per plot) at three-leaflet seedling, flowering and maturity. Weed density was recorded as the number of weeds in a 0.6 × 0.6 m quadrat (three quadrats per plot) at three-leaflet seedling, flowering and maturity. To assess both of Fusarium and Rhizoctonia root rot diseases, five bean plants per quadrat (0.6 m × 0.6 m; three quadrats per plot) were dug up randomly to detect typical disease symptoms (Naseri and Marefat, 2011) at three-leaflet seedling, flowering and maturity. The percentage of plants having Rhizoctonia red-brown cankers on roots per quadrat was recorded as Rhizoctonia disease incidence. In addition, the percentage of bean plants with discolored and rotted root tissues per quadrat was regarded as Fusarium disease incidence. In the laboratory, symptomatic roots were rinsed in tap water, surface-sterilized in 1% NaOCl for 2 min, and cultured onto potato dextrose agar (PDA, Difco, Detroit, MI). Fungal colonies were identified after 7–10 days incubation at room temperature (approx. 22 °C) according to the identification key provided by Barnett and Hunter (1998). To measure bean biomass, 10 plants which were assessed for root rots were used to determine bean plant dry matter per plot. At maturity, the number of pods per plant and seeds per pod were counted for 10 randomly assessed plants per plot. Then the height (cm) of bean plant was recorded for 10 plants assessed for the number of pods and seeds. Finally, six central rows of every plot with 3 m length were harvested to measure bean seed yield (kg/ha) per plot.
2.2. Statistical analysis
A total of 256 experimental plots were studied for the development of bean biomass and leaf area, Fusarium and Rhizoctonia root rot, weed biomass and density according to the best models fitted to the disease, bean and weed datasets. Due to the lack of or mild infestations resulting in model fitness difficulties, maximum fly ratings during the growing seasons were considered for statistical evaluations. To fit the best regression model, the seasonal assessments of either variable for the cultivars treated with diverse planting dates and weed management methods were subjected to the following standard models: exponential, logistic, Gompertz, linear-by-linear, quadratic-by-linear, and Gaussian (Kakhki et al., 2022, Table 2). Following the model fitness, the parameters estimated by specific models were regarded as indicators of the over-season development for bean biomass and leaf area, Fusarium and Rhizoctonia root rots, weed biomass and density. All the statistical procedures were performed using the GENSTAT (VSN International, Oxford, UK).
Table 2.
Models fitted to plant growth and production, Fusarium and Rhizoctonia root rots, fly and weed development datasets collected from bean cultivars differing in planting dates and weed management methods.
| Variables | Modelsa | Parameters estimated |
|---|---|---|
| Bean biomass | a + brx | Exponential parameter a |
| Exponential parameter b | ||
| Exponential parameter r | ||
| Bean leaf area | a + b∗Gauss ((x − m)/s) | Gaussian parameter a |
| Gaussian parameter b | ||
| Gaussian parameter m | ||
| Gaussian parameter s | ||
| Fusarium | a + b/(1 + dx) | Linear-by-Linear parameter a |
| root rot | Linear-by-Linear parameter b | |
| Linear-by-Linear parameter d | ||
| Rhizoctonia | a + b/(1 + dx) | Linear-by-Linear parameter a |
| root rot | Linear-by-Linear parameter b | |
| Linear-by-Linear parameter d | ||
| Weed biomass | a + brx | Exponential parameter a |
| Exponential parameter b | ||
| Exponential parameter r | ||
| Weed density | a + b/(1 + dx) | Linear-by-Linear parameter a |
| Linear-by-Linear parameter b | ||
| Linear-by-Linear parameter d |
x = Time intervals (days) between consecutive assessments.
The factor analysis (FA) was used to examine linkages of 27 indicators defined for the bean-disease-weed development and productivity to principal factors which received an eigenvalue or proportion of data variance ≥1.0. Each principal factor described a linear combination of those indicators involved. To predict bean production, it was required to reduce the number of indicators in order to minimizing collinearity among variables selected for the regression modeling. Indicators, which contributed significantly to principal factors, achieved moderate to high loading values (≥0.35; Kranz, 2003). In the next step, relationships between these significantly contributed indicators were examined using simple correlations. Then, the principal component analysis (PCA) using a correlation matrix was performed to evaluate contributions of bean growth, Fusarium and Rhizoctonia root rot diseases, fly, weed and bean productivity indicators in principal components (PCs). This PCA simplified the selection of significantly associated indicators and their interactions according to loading values (≥0.35; Kranz, 2003) to be involved in regression models for predicting pod and seed production, and bean yield. Briefly, the FA reduced the number of bean, disease and weed indicators from 27 (Table 3) to 11 (Table 4) to minimize collinearity. Then, the correlations and PCA determined associations between these 11 indicators for modeling. The stepwise variable selection method used the two criteria of adjusted coefficient of determination (R2) and Mallows Cp to fit the best variables and interactions (Brusco and Stahl, 2005). Furthermore, the graphical appraisal of normally distributed residuals, F-test and R2 were considered for the best models fitted (Kakhki et al., 2022).
Table 3.
Factor analysis of plant growth and production, Fusarium and Rhizoctonia root rots, fly and weed development in bean cultivars differing in planting dates and weed management methods.
| Variables | Factors |
||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | ||
| Bean biomass | Exponential parameter a | −0.25 | 0.17 | 0.06 | −0.31 | 0.08 | 0.41 | −0.04 | 0.16 | −0.15 | 0.07 |
| Exponential parameter b | 0.23 | −0.17 | −0.06 | 0.32 | −0.08 | −0.41 | 0.03 | −0.17 | 0.16 | −0.07 | |
| Exponential parameter r | −0.19 | −0.00 | 0.16 | 0.15 | 0.18 | −0.06 | −0.48 | −0.10 | −0.21 | −0.07 | |
| Bean leaf area | Gaussian parameter a | −0.44 | 0.06 | −0.05 | 0.06 | −0.07 | −0.06 | 0.19 | −0.04 | 0.05 | −0.09 |
| Gaussian parameter b | 0.45 | −0.05 | 0.04 | −0.06 | 0.07 | 0.04 | −0.17 | 0.06 | −0.06 | 0.09 | |
| Gaussian parameter m | −0.05 | 0.11 | −0.18 | −0.12 | 0.02 | −0.17 | −0.08 | 0.46 | −0.18 | 0.13 | |
| Gaussian parameter s | 0.41 | −0.02 | −0.07 | −0.13 | 0.06 | −0.04 | −0.20 | 0.18 | −0.02 | 0.14 | |
| Maximum leaf area | 0.25 | 0.00 | 0.03 | 0.06 | −0.12 | 0.09 | 0.40 | −0.06 | −0.28 | −0.06 | |
| Fusarium | Linear-by-Linear parameter a | 0.14 | −0.01 | −0.27 | 0.02 | 0.26 | 0.10 | 0.47 | 0.07 | 0.22 | 0.02 |
| root rot | Linear-by-Linear parameter b | −0.01 | 0.03 | 0.20 | −0.17 | −0.60 | −0.13 | −0.15 | 0.01 | −0.09 | −0.07 |
| Linear-by-Linear parameter d | −0.07 | −0.02 | −0.04 | 0.20 | 0.58 | 0.08 | −0.19 | −0.07 | −0.06 | 0.08 | |
| Maximum disease incidence | 0.00 | 0.30 | 0.07 | −0.01 | −0.09 | −0.16 | −0.05 | 0.03 | 0.28 | 0.33 | |
| Rhizoctonia | Linear-by-Linear parameter a | −0.07 | −0.17 | −0.21 | −0.39 | 0.10 | −0.26 | 0.00 | −0.34 | −0.15 | 0.24 |
| root rot | Linear-by-Linear parameter b | 0.07 | 0.17 | 0.23 | 0.41 | −0.10 | 0.27 | 0.02 | 0.33 | 0.14 | −0.04 |
| Linear-by-Linear parameter d | 0.02 | −0.07 | −0.10 | −0.17 | 0.04 | −0.05 | −0.10 | −0.03 | 0.02 | −0.75 | |
| Weed biomass | Exponential parameter a | −0.05 | −0.31 | 0.25 | −0.18 | 0.15 | −0.16 | 0.01 | 0.31 | 0.28 | −0.09 |
| Exponential parameter b | 0.07 | 0.35 | −0.20 | 0.21 | −0.12 | 0.10 | −0.04 | −0.30 | −0.27 | 0.09 | |
| Exponential parameter r | 0.04 | 0.20 | 0.14 | −0.20 | 0.02 | −0.03 | −0.13 | −0.20 | 0.50 | 0.16 | |
| Maximum weed biomass | 0.15 | 0.29 | 0.10 | −0.28 | 0.04 | 0.11 | 0.09 | −0.20 | 0.14 | −0.11 | |
| Weed density | Linear-by-Linear parameter a | 0.20 | 0.22 | 0.09 | −0.33 | 0.11 | 0.04 | 0.08 | 0.12 | −0.16 | −0.15 |
| Linear-by-Linear parameter b | 0.03 | −0.25 | 0.45 | 0.01 | 0.09 | 0.06 | 0.21 | −0.14 | −0.11 | 0.12 | |
| Linear-by-Linear parameter d | −0.02 | 0.20 | −0.51 | 0.02 | −0.04 | −0.02 | −0.11 | 0.12 | 0.17 | −0.12 | |
| Bean height | 0.11 | −0.20 | −0.05 | −0.10 | −0.07 | 0.34 | −0.06 | −0.23 | 0.08 | −0.06 | |
| Maximum fly | 0.11 | −0.20 | −0.15 | −0.02 | −0.09 | −0.11 | 0.01 | 0.26 | −0.24 | 0.15 | |
| Pod no./Plant | 0.20 | −0.21 | −0.14 | 0.06 | −0.12 | 0.31 | −0.33 | −0.04 | 0.18 | −0.03 | |
| Seed no./Pod | −0.06 | −0.28 | −0.13 | −0.03 | −0.17 | 0.36 | −0.05 | −0.05 | 0.01 | 0.14 | |
| Yield (kg/ha) | −0.22 | −0.29 | −0.21 | −0.07 | −0.18 | 0.13 | 0.05 | 0.08 | 0.17 | 0.16 | |
| Eigenvalues | 4.18 | 3.30 | 2.72 | 2.34 | 2.05 | 1.79 | 1.65 | 1.39 | 1.16 | 1.07 | |
| Variation (%) | 15.48 | 12.23 | 10.07 | 8.65 | 7.59 | 6.63 | 6.11 | 5.16 | 4.28 | 3.97 | |
| Accumulated variation (%) | 15.5 | 27.7 | 37.8 | 46.4 | 54.0 | 60.6 | 66.8 | 71.9 | 76.2 | 80.2 | |
aA bold number indicates a significant loading value ≥0.35.
Table 4.
Correlations between best predictors of plant growth and production, Fusarium and Rhizoctonia root rots, fly and weed development in bean cultivars differing in planting dates and weed control methods.
| Descriptors | BBba | BLAb | FRRb | RRRb | WBb | WDd | BH | MF | PP | SP | Y | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Bean biomass | Exponential parameter b | 1.00 | ||||||||||
| Bean leaf area | Gaussian parameter b | 0.28b | 1.00 | |||||||||
| Fusarium root rot | Linear-by-Linear parameter b | −0.03 | −0.02 | 1.00 | ||||||||
| Rhizoctonia root rot | Linear-by-Linear parameter b | 0.03 | 0.06 | 0.03 | 1.00 | |||||||
| Weed biomass | Exponential parameter b | 0.01 | 0.04 | −0.02 | 0.18 | 1.00 | ||||||
| Weed density | Linear-by-Linear parameter d | −0.01 | −0.11 | −0.18 | −0.11 | 0.34 | 1.00 | |||||
| Bean height | 0.06 | 0.15 | −0.02 | −0.09 | −0.12 | −0.05 | 1.00 | |||||
| Maximum fly | 0.23 | 0.20 | 0.01 | −0.10 | −0.08 | −0.01 | 0.07 | 1.00 | ||||
| Pod no./Plant | 0.16 | 0.45 | 0.01 | 0.05 | 0.00 | 0.11 | 0.45 | 0.09 | 1.00 | |||
| Seed no./Pod | −0.06 | −0.07 | 0.01 | −0.09 | −0.17 | −0.02 | 0.39 | 0.24 | 0.31 | 1.00 | ||
| Yield (kg/ha) | −0.12 | −0.36 | 0.03 | −0.26 | −0.26 | 0.10 | 0.09 | 0.24 | 0.17 | 0.55 | 1.00 |
Descriptors were abbreviated according to initials of their names and parameters.
Bold numbers refer to significance at 0.05 probability level.
3. Results
Figure 1 summarized the statistical methods used in this plot-scale study to describe the crop productivity according to the complex interactions between bean growth, Fusarium and Rhizoctonia root rots, fly and weed development predictors.
Figure 1.
A flowchart demonstrating main study statistical methods and outcomes.
3.1. Factor analysis
From the FA, the ten principal factors accounted for 80.2% of the variation in bean growth, root rot diseases, weed and productivity data obtained from the cultivars planted at various planting dates and weed management treatments over the two seasons (Table 3). The first principal factor justified 15.5% of the total data variance. This factor provided the positively moderate loadings for the relevance of the Gaussian parameter b and s of bean leaf area, and a negatively moderate loading for Gaussian parameter a of this bean growth variable. Thus, this factor of the FA was defined as bean leaf area factor. The second factor accounting for 12.2% of the data variability provided a positively significant loading for the correspondence of the exponential parameter b of weed biomass. This suggested the second factor to be defined as the weed biomass factor. The third principal factor, explaining 10.1% of the data variance, represented the positive and negative loading values for the linear-by-linear parameter b and d, respectively, estimated for the weed density indicator. Thus, this factor of FA test was defined as the weed density factor. This forth factor, which accounted for 8.7% of the data variance, suggested the associations of the linear-by-linear parameter a and b estimated for the incidence of Rhizoctonia root rot that was defined as the Rhizoctonia factor. The fifth factor, which provided the relevance of the linear-by-linear parameter b and d of the Fusarium root rot incidence, was named as the Fusarium factor.
The exponential parameter a and b of bean biomass and the indicator of seed number per pod significantly contributed in the sixth principal factor, suggesting it as the factor of bean biomass and seed production. The exponential parameter r of bean biomass, the indicator of maximum leaf area and the linear-by-linear parameter a of the Fusarium root rot significantly corresponded with the seventh principal factor, defining it as the factor of bean growth and Fusarium disease. The eighth factor added the significant contribution of the Gaussian parameter m of bean leaf area to the significant contributions of the other three Gaussian parameters of this plant growth indicator in the first principal factor. The ninth factor added the linkage of the exponential parameter r of weed biomass to the linkage of the exponential parameter b of this weed indicator determined by the second factor. The tenth factor provided the contribution of the linear-by-linear parameter d of Rhizoctonia root rot that was added to the contributions of the parameters a and b estimated by the linear-by-linear disease progress curve in the forth factor.
According to this FA (Table 3), the indicators with greater loadings among those values provided for the indicators of bean, disease and weed development were considered for the remainder of statistical analyses. If there were more than one significant loading value for an indicator, then the greater loading provided by the earliest principal factor was considered. For instance, the three exponential parameters of bean biomass significantly contributed in the sixth and seventh factors, however, the lower loading values of parameters a and b were selected due to their relevance to the sixth factor. Because the sixth factor accounted for a greater percentage (7%) of total data variance compared to the seventh factor (6%). In the next step, the exponential parameter b with a loading value equal to the parameter a was selected because of having a greater sum of loading values over the ten principal factors. Therefore, this FA identified the most descriptive indicators of the over-season development of bean, disease and weed in bean cultivars differing in planting date and weed management treatments (Table 3).
3.2. Correlation analysis
From the correlation analysis results, there was a positive relationship (P ≤ 0.05) between the exponential and the Gaussian parameters b estimated for the development of bean biomass and leaf area, respectively (Table 4). The Gaussian parameter b of bean leaf area was correlated (P ≤ 0.05) to the pod number per plant and yield variables. The linear-by-linear parameter b of Rhizoctonia root rot development over the two growing seasons negatively corresponded (P ≤ 0.05) with the yield variable. The exponential parameter b of weed biomass corresponded (P ≤ 0.05) positively with the linear-by-linear parameter d of weed density and negatively with the yield variable. Bean Height was positively linked (P ≤ 0.05) to the variables defined for the number of pods per plant and seeds per pod. The seed number per pod was correlated (P ≤ 0.05) positively with the number of pods per plant and bean yield (Table 4).
3.3. Principal component analysis
From the PCA test, the five principal components explained 69.5% of variations in bean growth, fly, Fusarium and Rhizoctonia root rots, weed and bean production datasets obtained from the bean cultivars differing in the planting date and weed management method during the two seasons (Table 5). The first PC accounting for 20.3% of the total data variance provided the negatively moderate loadings for the correspondence of bean production indicators, height, pod number per plant, seed number per pod and yield. Thus, this PC signified the negative contributions of bean productivity indicators. According to the second PC accounting for 16.8% of the dataset variance, the exponential parameter b of bean biomass, the Gaussian parameter b of bean leaf area and the pod number per plant positively corresponded with this PC. Bean yield was negatively associated with the second PC. Therefore, this PC determined the indirect relationship of the biomass increase factor (exponential parameter b), the height of leaf area curve peak (Gaussian parameter b) and pod number per plant with bean yield. This PC demonstrated the association of a lower exponential parameter b (a less effective bean biomass increase factor) and Gaussian parameter b (a shorter leaf area curve's peak), and fewer pods per plant with a higher bean yield.
Table 5.
Principal component analysis of plant growth and production, Fusarium and Rhizoctonia root rots, fly and weed predictors selected according to factor analysis.
| Variables | Principal components |
|||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| Bean biomass | Exponential parameter b | −0.07 | 0.39 | 0.04 | 0.47 | −0.09 |
| Bean leaf area | Gaussian parameter b | −0.08 | 0.61 | 0.11 | 0.07 | 0.11 |
| Fusarium root rot | Linear-by-Linear parameter b | −0.01 | −0.04 | 0.35 | −0.17 | −0.69 |
| Rhizoctonia root rot | Linear-by-Linear parameter b | 0.20 | 0.23 | 0.08 | −0.37 | −0.38 |
| Weed biomass | Exponential parameter b | 0.26 | 0.18 | −0.52 | −0.12 | −0.37 |
| Weed density | Linear-by-Linear parameter d | 0.02 | −0.03 | −0.74 | 0.09 | −0.07 |
| Bean height | −0.41 | 0.20 | −0.01 | −0.36 | 0.24 | |
| Maximum fly | −0.30 | 0.11 | 0.03 | 0.58 | −0.32 | |
| Pod no./Plant | −0.39 | 0.39 | −0.17 | −0.29 | −0.02 | |
| Seed no./Pod | −0.53 | −0.13 | −0.06 | −0.17 | −0.15 | |
| Yield (kg/ha) | −0.44 | −0.41 | −0.12 | 0.08 | −0.19 | |
| Eigenvalues | 2.23 | 1.85 | 1.39 | 1.17 | 1.01 | |
| Variation (%) | 20.30 | 16.78 | 12.61 | 10.63 | 9.16 | |
| Accumulated variation (%) | 20.3 | 37.1 | 49.7 | 60.3 | 69.5 | |
A bold number indicates a significant loading value ≥0.35.
The third PC, which justified 12.6% of the total variance in the bean-disease-fly-weed dataset, provided the positively moderate loading for the Fusarium root rot incidence and negatively high loading values for the weed biomass and density indicators (Table 5). This PC demonstrated the indirect relationship of Fusarium root rot increase factor (linear-by-linear parameter b) with the weed biomass (exponential parameter b) and density (linear-by-linear parameter d) increase factors. Therefore, the third PC suggested the association of a lower linear-by-linear parameter b (a less effective Fusarium root rot progress) with a greater exponential parameter b (a more effective weed biomass increase) and linear-by-linear parameter d (a faster weed density increase rate).
The forth PC justifying 10.6% of the total data variance detected the significantly positive contributions of the maximum fly and bean biomass, and negative contributions of Rhizoctonia root rot and bean height indicators (Table 5). This suggested the indirect associations of the linear-by-linear parameter b of Rhizoctonia root rot and bean height with the exponential parameters b of bean biomass and maximum fly. Thus, the forth PC determined that a lower Rhizoctonia root rot increase factor (a less effective disease progress) and shorter bean plants corresponded with a greater bean biomass increase factor (a more effective bean biomass increase) and a higher fly infestation level.
The fifth PC explained 9.2% of the total variance in dataset and detected significantly negative contributions of the indicators described for Fusarium and Rhizoctonia root rots, and weed biomass (Table 5). Furthermore, the fifth PC received a high contribution from Fusarium root rot and moderate contributions from Rhizoctonia root rot and weed biomass. This PC also demonstrated the direct relationships of the linear-by-linear parameters b estimated for the incidence of Fusarium and Rhizoctonia root rots, and the exponential parameter b estimated for weed biomass. Therefore, this PC suggested the correspondence of greater Fusarium and Rhizoctonia increase factors (more effective root rots increase) with a greater weed biomass increase factor (a more effective weed biomass development).
3.4. Regression analysis
The multivariate regression analyses explained 59–63% of variations in pod (F probability = 0.001; R2 = 0.60) and seed (F probability = 0.001; R2 = 0.59) production, and yield (F probability = 0.001; R2 = 0.63) in bean cultivars treated with various planting dates and weed management methods across 256 plots according to the bean growth, fly, Fusarium and Rhizoctonia root rots, and weed development indicators (Table 6). The indicators and their interactions were defined to be involved in regression models based on the PCA results, followed by the stepwise selection. The linear combination of singular and interactions of following eight indicators: bean biomass, height and leaf area, Fusarium and Rhizoctonia root rots, maximum fly infestation, weed biomass and density, corresponded significantly with the number of pod/plant and seed/pod, and yield (kg/ha) in the bean cultivars examined during the two growing seasons. Using a simple regression analysis, the observed data for bean yield corresponded significantly to the data fitted by the yield model (Figure 2). Therefore, the crop yield was estimated using the predictors described for the over-season development of bean, disease, fly and weed studied at field-plot scale.
Table 6.
Multiple regression analysis of bean productivity according to plant growth, Fusarium and Rhizoctonia root rots, fly and weed predictors (R2 for pod model = 60%; R2 for seed model = 59%; R2 for yield model = 63%).
| Variables | Models | Parameter estimates | Standard errors | t-probability |
|---|---|---|---|---|
| Bean biomass EXPb × Weed biomass EXPb | Pod no./Plant | 0.000 | 0.000 | 0.175 |
| Seed no./Pod | 0.000 | 0.000 | 0.156 | |
| Yield (kg/ha) | 0.006 | 0.007 | 0.361 | |
| Fusarium root rot LLb × Weed biomass EXPb × Weed density LLd | Pod no./Plant | 0.000 | 0.000 | 0.389 |
| Seed no./Pod | 0.000 | 0.000 | 0.301 | |
| Yield (kg/ha) | 0.006 | 0.006 | 0.363 | |
| Fusarium root rot LLb × Rhizoctonia root rot LLb × Weed biomass EXPb | Pod no./Plant | 0.000 | 0.000 | 0.482 |
| Seed no./Pod | 0.000 | 0.000 | 0.382 | |
| Yield (kg/ha) | 0.001 | 0.001 | 0.434 | |
| Bean biomass EXPb × Bean leaf area GAUb | Pod no./Plant | −0.000 | 0.000 | 0.470 |
| Seed no./Pod | −0.000 | 0.000 | 0.265 | |
| Yield (kg/ha) | −0.000 | 0.000 | 0.148 | |
| Rhizoctonia root rot LLa × Height | Pod no./Plant | −0.001 | 0.000 | <0.001 |
| Seed no./Pod | −0.000 | 0.000 | 0.004 | |
| Yield (kg/ha) | −0.182 | 0.045 | <0.001 | |
| Maximum fly | Pod no./Plant | 0.808 | 0.098 | <0.001 |
| Seed no./Pod | 0.316 | 0.039 | <0.001 | |
| Yield (kg/ha) | 279.200 | 32.300 | <0.001 | |
| Maximum fly × Rhizoctonia root rot LLb | Pod no./Plant | 0.002 | 0.001 | <0.001 |
| Seed no./Pod | 0.001 | 0.000 | 0.004 | |
| Yield (kg/ha) | 0.776 | 0.207 | <0.001 |
EXPb= exponential parameter b; EXPd= exponential parameter d; GAUb= Gaussian parameter b; LLb = Linear-by-Linear parameter b.
Figure 2.
Simple regression analysis (r = 0.37; F probability = 0.003) of observed data against bean yield data fitted by multiple regression model.
4. Discussion
Sustainable bean production seems dependent on precise monitoring and measuring bean growth in interaction with the spread of major diseases, pests and weeds (Naseri, 2019a). To meet this requirement, it was attempted in the present study to examine the associations of over-season fluctuations in bean biomass, height and leaf area, fly infestation, Fusarium and Rhizoctonia root rots, weed biomass and density with the crop productivity in the cultivars treated with various planting dates and weed management practices at the scale of experimental plots. Hence, it was firstly desired to detect the best indicators of bean growth, fly infestation, root rot diseases and weed development in early to very late sown bean cultivars treated with various weed management methods during two growing seasons across 256 field plots. This experimental design diversified the progression of bean, diseases, fly and weed over season due to treating experimental plots with different planting dates and weed management methods (Kakhki et al., 2022). It is believed that such a considerable variation in the dataset improves the predictive value of a disease progress curve element (Kranz, 2003). In the current study, the various parameters estimated by the best models were considered as the curve elements of bean, disease and weed development. Therefore, these specific indicators described according to the standard models fitted to the seasonal datasets of bean biomass and leaf area, Fusarium and Rhizoctonia root rots, weed biomass and density (Kakhki et al., 2022) were examined for their predictive values. In fact, such best models described fluctuations and variations in bean, disease and weed development during the growing seasons. However, it was strictly required to reduce the number of indicators in order to overcome the collinearity among 27 variables before involvement in the multivariate regression analysis. The FA has been used in a large number of previous studies as a dimension reduction technique to determine the most predictive variables (Raiesi and Kabiri, 2016). The present FA determined the most relevant indicators of bean growth and production, fly, root rot and weed development in highly diverse bean crops. To the best of our knowledge, the present field-scale findings determined the considerable predictive values of bean, root rot, fly and weed development variables for the first time. This could improve the accuracy of future monitoring and estimating seasonal patterns of bean growth and production, fly infestation, Fusarium and Rhizoctonia root rots, and weed, and thus, to develop more effective integrated crop management programs.
Campbell et al. (1980) reported a final disease severity and first-difference regression linear coefficient as specific elements of bean root rot progress curves. In Nigeria, Chikoye et al. (2006) examined the efficiency of Atrazine herbicide to reduce weed populations in maize fields according to an exponential model. However, none of earlier studies simulated the jointly seasonal progression of bean growth, fly, root rot and weed to determine the best indicators fitted to field-scale datasets. Thus, the present multiple regression analysis of bean yield using a linear combination of the exponential, Gaussian and linear-by-linear parameters as the best indicators of over-season development of bean biomass and leaf area, Fusarium and Rhizoctonia root rots, and weed biomass and density appears to be the first report. Such information must add a further value to future epidemiological studies on seasonal variability of bean pest, disease and weed for crop yield improvement purposes. Because these best models estimated parameters, which are to be used as bean, disease and weed progress curve elements in future research, simplified the interpretation of multiple-point field assessments. Furthermore, single-point assessments are never considered as informative as multiple-point assessments (Naseri and Tabande, 2017) of bean, disease and weed development during growing seasons. Moreover, the high variability in the present bean growth and production, fly, root rot and weed datasets following the treatment of experimental plots with influential planting dates and weed management practices (Kakhki et al., 2022) may signify the necessity of incorporating a well-timed planting date as an environmental-friendly disease and weed management strategy into future bean production programs.
Linkages of bean production with agro-ecological conditions (Naseri, 2019a), bean growth (Manschadi et al., 1998) and fly (Talekar 1992), Fusarium and Rhizoctonia root rots (Campbell et al., 1980), weed populations (Ghamari and Ahmadvand, 2012) had been documented previously. Furthermore, El-Dabaa et al. (2019) looked at the combined management of disease and weed when reported that the treatment of two faba bean cultivars with Clethodim (herbicide) and Trichoderma spp. (biocontrol agent) decreased Rhizoctonia root rot and weed, and then, improved plant growth and yield. However, none of earlier documents simulated bean productivity according to the best indicators of plant growth, disease, fly and weed dynamics over the time. To the best of our knowledge, explaining 63% of variations in bean yield (kg/ha) based on the regression modeling of eight predictors defined in the current two-year research at field scale is reported for the first time. In addition to the above-mentioned findings, this study demonstrated the significantly descriptive values of bean biomass, height and leaf area, fly, Fusarium and Rhizoctonia root rots, weed biomass and density variables when jointly contributed in the crop productivity. The PCA results also suggested the significant associations of bean growth with yield, Fusarium and Rhizoctonia root rots with weed, and fly infestations with bean growth and disease. Although these associations had been individually reported for bean, disease, fly and weed variables (Naseri, 2019a, Naseri, 2019b), this combined interaction of variables in the form of a regression model developed based on the PCA is novel. Further to these PCA findings, the noticeable associations of root rot diseases with the weed variable highlighted the necessity of a proper choice of herbicides with higher weed control efficiency. This not only reduces weed populations, but also decreases root rots, and thus, improves bean productivity. Such information may advance the current understanding of the complex interaction of bean yield with disease, pest and weed.
5. Conclusion
The current research determined the linkages of within and over-season variations in bean growth (biomass, height and leaf area), fly infestation, Fusarium and Rhizoctonia root rots, weed development (biomass and density) with the yield and seed/pod production in the cultivars sown at different planting dates and managed with various weed control methods at a plot scale. Detecting the best indicators of bean-fly-disease-weed variability across different cultivars, planting dates and weed management methods may improve the accuracy of future predictions of crop, pest, disease and weed development to develop more influential integrated crop management programs. Moreover, a well-timed planting date in late spring can improve herbicide efficiency and reduce root rot intensity and weeds from environmental-friendly bean farming viewpoints.
Declarations
Author contribution statement
Seyed Hossein Nazer Kakhki: Conceived and designed the experiments; Performed the experiments.
Mohamad Vali Taghaddosi, Mohamad Rahim Moini: Contributed reagents, materials, analysis tools or data.
Bita Naseri: Analyzed and interpreted the data; Wrote the paper.
Funding statement
Dr. Bita Naseri and Seyed Hossein Nazer Kakhki were supported by areeo [4-47-16-88113 & 4-47-16-90070].
Data availability statement
Data will be made available on request.
Declaration of interest’s statement
The authors declare no conflict of interest.
Additional information
No additional information is available for this paper.
References
- Brusco M.J., Stahl S. Statistics and Computing. Springer; New York: 2005. Branch-and-bound applications in combinatorial data analysis. [Google Scholar]
- Barnett H.L., Hunter B.B. The American Phytopathological Society; St Paul, MN: 1998. Illustrated Genera of Imperfect Fungi. [Google Scholar]
- Campbell C.L., Madden L.V., Pennypacker S.P. Structural characterization of bean root rot epidemics. Phytopathology. 1980;70:152–155. [Google Scholar]
- Chikoye D., Udensi U.E., Lum A.F. Performance of a new formulation of atrazine for weed control in maize in Nigeria. J. Food Agric. Environ. 2006;4:1–4. [Google Scholar]
- Dehghani A., Panjehkeh N., Asadi Rahmani H., Salari M., Darvishnia M. Effectiveness of simultaneous application of indigenous rhizobium and arbuscular mycorrhiza on root rot disease and yield of red bean (Phaseolus vulgaris L.) in Lorestan Province. Biocontrol Plant Protect. 2018;6:43–58. [Google Scholar]
- El-Dabaa M.A.T., Abd-El-Khair H., El-Nagdi W.M.A. Field application of Clethodim herbicide combined with Trichoderma spp. for controlling weeds, root knot nematodes and Rhizoctonia root rot disease in two faba bean cultivars. J. Plant Protect. Res. 2019;59:255–264. [Google Scholar]
- Ghamari H., Ahmadvand G. Weed interference affects dry bean yield and growth. Not. Sci. Biol. 2012;4:70–75. [Google Scholar]
- Greathead D.J. A study in East Africa of the bean flies (Diptera: Agromyzidae) affecting Phaseolus vulgaris and their natural enemies, with the description of a new species of Melana gromyza Hend. Bull. Entomol. Res. 1968;59:541–561. [Google Scholar]
- Kakhki S.H., Moini M.R., Naseri B. Forecasting bean yield losses under weed and Fusarium impacts from field plot statistical modeling. Rhizosphere. 2022;21 [Google Scholar]
- Kranz J. Comparative Epidemiology of Plant Diseases. Springer; Berlin, Heidelberg: 2003. Comparison of temporal Aspects of epidemics: the disease progress curves. [Google Scholar]
- Manschadi A.M., Sauerborn Stutzel J.H., Gobel W., Saxena M.C. Simulation of faba bean (Vicia faba L.) growth and development under Mediterranean conditions: model adaptation and evaluation. Eur. J. Agron. 1998;9:273–293. [Google Scholar]
- Naseri B. Bean production and Fusarium root rot in diverse soil environments in Iran. J. Soil Sci. Plant Nutr. 2014;14:177–188. [Google Scholar]
- Naseri B. In: Sustainable Management of Soil and Environment. Meena Ram Swaroop, Kumar Sandeep, Singh Jitendra, Lal Mangi., editors. Springer; 2019. Legume root rot control through soil management for sustainable agriculture. [Google Scholar]
- Naseri B. In: Organic Farming. Unni M.R., Sarathchandran Veloormadom C., Thomas Sabu, editors. Elsevier; 2019. The potential of agro-ecological properties in fulfilling the promise of organic farming: a case study of bean root rots and yields in Iran. [Google Scholar]
- Naseri B., Marefat A. Large-scale assessment of agricultural practices affecting Fusarium root rot and common bean yield. Eur. J. Plant Pathol. 2011;131:179–195. [Google Scholar]
- Naseri B., Moradi P. Farm management strategies and the prevalence of Rhizoctonia root rot in bean. J. Plant Dis. Prot. 2015;5:238–243. [Google Scholar]
- Naseri B., Mousavi S.S. The development of Fusarium root rot and productivity according to planting date and depth, and bean variety. Australas. Plant Pathol. 2013;42:133–139. [Google Scholar]
- Naseri B., Tabande L. Patterns of Fusarium wilt epidemics and bean production determined according to a large-scale dataset from agro-ecosystems. Rhizosphere. 2017;3:100–104. [Google Scholar]
- Naseri B., Younesi H. Beneficial microbes in biocontrol of root rots in bean crops: a meta-analysis (1990-2020) Physiol. Mol. Plant Pathol. 2021;116 [Google Scholar]
- Ojwang P.P.O., Melis R., Songa J.M., Githiri M. Genotypic response of common bean to natural field populations of bean fly (Ophiomyia phaseoli) under diverse environmen-tal conditions. Field Crop. Res. 2010;117:139–145. [Google Scholar]
- Oveisi M., Pourmorad Kaleibar B., Rahimian Mashhadi H., Müller-Schärer H., Bagheri A., Amani M., Elahinejad M., Masoumi D. Bean cultivar mixture allows reduced herbicide dose while maintaining high yield: a step towards more eco-friendly weed management. Eur. J. Agron. 2021;122 [Google Scholar]
- Raiesi F., Kabiri V. Identification of soil quality indicators for assessing the effect of different tillage practices through a soil quality index in a semi-arid environment. Ecol. Indicat. 2016;71:198–207. [Google Scholar]
- Sariah J.B., Makundi R.H. Effect of sowing time on infestation of beans (Phaseolus vulgaris L.) by two species of the Bean Stem Maggot, Ophiomyia spencerella and Ophiomyia phaseoli (Diptera: Agromyzidae) Arch. Phytopathol. Plant Protect. 2007;40:45–51. [Google Scholar]
- Tabande L., Naseri B. How strongly rhizobial nodulation is associated with bean cropping system? J. Plant Protect. Res. 2020;60:176–184. [Google Scholar]
- Talekar N.S. In: Cartillas para CIAL Los comités de investigación Agrícola local Cartilo. 2. Cali. Henry G., Janseen W., editors. CIAT; 1992. The bean fly pest complex of snap beans in the tropics. [Google Scholar]
- Trutmaun P., Graf W. The impact of pathogens and arthropod pests on common bean production in Rwanda. Int. J. Pest Manag. 1993;39:328–333. [Google Scholar]
- Van der Goot P. Asian Vegetable Research and Development Center (AVRDC); Shanhua, Taiwan: 1930. Agromyzid Flies of Some Native Legume Crops in Java; p. 98. [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data will be made available on request.