Relationship Between the Morphometric and Nutritional Variables of Bananas (Musa AAA, Cavendish cv. Williams Subgroup) and the Formation of Maturity Bronzing

Abstract

Bananas (Musa AAA) can be affected by maturity bronzing, a physiological disorder that appears in the form of bronzing on the fruit’s epidermis, compromising its quality and generating economic losses due to commercial rejection. Since the symptoms are only evidenced at harvest, it is necessary to identify the associated factors in order to develop preventive strategies. This research analyzed morphometric and nutritional characteristics potentially related to the formation of stains on two banana-exporting farms in Antioquia and Magdalena (Colombia). Monitoring plots were established, in which 310 productive units with the banana Cavendish cv. Williams subgroup were evaluated over 52 weeks. In all units, the height, the pseudostem perimeter, the weight and number of hands of the bunch, and the weight of the affected fruit were recorded. In addition, foliar and soil analyses were conducted in each production unit, and some climatic components were characterized. Through a multiple logistic regression model, it was observed that a pseudostem perimeter smaller than 70 cm (measured 50 cm from the base), together with foliar B and Zn concentrations below 100 and 25 mg/kg, respectively, was associated with a higher probability of bronzing formation. These values should be interpreted as preliminary associations derived under specific conditions, and therefore as requiring validation across different contexts and management systems, before being considered as reference parameters. These findings provide new factors associated with maturity bronzing and open opportunities for future research aimed at its prevention.

1. Introduction

In the global market, musaceae are among the fruits with the highest production, consumption, and commercialization. Bananas are globally significant fruits cultivated in more than 140 countries [1]. Within this group, the Cavendish banana, mainly cultivated in tropical and subtropical regions, reaches an annual production volume of approximately 50 million tons [2]. Bananas serve not only as a staple food for over 400 million smallholder farmers but also as a major source of their income [3]. Banana farming for export is one of the main economic activities of the agricultural sector in Colombia, a country that ranks among the most important in the world with regard to banana production and export [4,5]. In 2023, approximately 106 million boxes (20 kg) were commercialized, amounting to USD 969 million, and the average productivity was 2.023 boxes (20 kg) per hectare [6].

However, in banana farming, a diversity of disorders affecting production and quality may appear. Physiological disorders are caused by nonpathogenic factors—including environmental stress, nutrient deficiencies or toxicities, chemical exposure, and certain genetic traits—that lead to abnormal external or internal fruit conditions and irregular growth patterns [7]. These disorders affect fruit quality and reduce market value [8,9]. One of them is maturity bronzing (MB) or maturity staining, regarded as a serious problem in several producing regions around the world, including Colombia [10,11]. This physiological disorder manifests in the form of a reddish or brown bronzing on the outer surface of the fruit (Figure 1), mostly at the tips of the hands; this is evident at the time of harvest or close to it [9,12,13]. This disorder does not affect production or organoleptic quality, but it does considerably affect commercial quality [9,14], generating significant economic losses [10,11,12]. It has been reported that MB may be visible starting from the ninth week of fruit development, i.e., in the weeks ahead of harvest [12,15], which does not allow for preventive action to reduce losses. Additionally, although previous studies exist, the current literature still does not allow for a full understanding of its causes or management [9,11,12,14,15].

The causes of MB are still unknown. Its symptoms seem to be the result of an undefined stress on the outer layers of the banana fruit epidermis, followed by rapid fruit growth and expansion. This condition has also been associated with water deficits at the time of bunch emergence during periods of rapid growth in humid and warm climates [9,12,13]. It has also been reported that MB is associated with calcium (Ca) deficiencies during the floral differentiation of the apical meristem, which triggers a decline in quality [10,11,12,15,16,17]. The relationship between the symptoms and Ca is presumed to be based on the structural role of this nutrient in membranes [14,18]; as deficiencies manifest in the fruits, the membranes lose their ability to expand, and they disintegrate easily [14,19]. The authors of [12,14,15] report that MB may be related to the climate, Ca, and the diameter of the fruit fingers. This indicates that the possible causes of MB are diverse and complex, as they at least involve the climate, mineral nutrition, and the morphometric characteristics of the fruit.

Mineral elements availability, in addition to being related to MB, is also one of the factors associated with low yields. Hence, nutrition is one of the most important factors in increasing the production and quality of a crop, in order to obtain bunches with the conditions and characteristics demanded by the international market [20,21]. In bananas, mineral nutrition constraints reduce yields and also cause various physiological disorders in addition to MB [22]. In this sense, research on mineral nutrition and fertilization issues is a priority in banana farming [5,13].

Although some factors associated with MB have been individually studied, a simultaneous and multifactorial analysis is still lacking; consequently, the current understanding of MB remains incomplete for ensuring effective prevention. Therefore, the objective of this research was to evaluate the incidence of MB and its association with the nutritional content of the foliar tissue, crop morphometric variables, and some climatic variables, in two banana-exporting regions of Colombia, i.e., the departments of Antioquia (Urabá) and Magdalena.

2. Materials and Methods

2.1. Location and Sampling of the Productive Units

This study was conducted on two banana farms (Musa AAA, Cavendish cv. Williams subgroup), with two monitoring and evaluation plots established on each farm. On the first farm, San Bartolo, located in Chigorodó, department of Antioquia (coordinates: 7.7073951° N, −76.7103789° E), at an altitude of 31.34 MASL, plots San Bartolo-1 (SB1) and San Bartolo-2 (SB2) were established, where 67 and 69 productive units (PUs) were assessed, respectively, between week 33 of 2018 and week 34 of 2019, through 11 samplings conducted at approximately five-week intervals. On the second farm, California, located in Zona Bananera, in the township of Guacamayal, in the department of Magdalena (coordinates: 10.7654281° N, −74.2000294° E), at an altitude of 30.21 MASL, monitoring plots California-1 (C1) and California-2 (C2), 89 and 85 PUs were evaluated, respectively. Monitoring was carried out between week 37 of 2018 and week 35 of 2019, with 10 samplings performed at six-week intervals, approximately.

Monitoring and sampling were carried out for each PU, which comprised the plant with the bunch, known as the mother plant or the main plant, and the other developing plant, called the sucker, which stems from a lateral bud. Each PU underwent monitoring throughout the evaluation period. Once the mother plant had been harvested, the sucker was coded. The emerging bunch of the mother plant was marked on the stem until harvest, with the purpose of identifying the PU of origin of each bunch during postharvest.

It is important to note that the results of this study apply within the context of the management practices and specific environmental conditions of each farm where this observational study was conducted.

2.2. Monitoring Variables

In each sampling, all the PUs in each plot were evaluated. Once the mother plant had been harvested, measurement proceeded with the sucker. Additionally, during postharvest, the harvested bunch was evaluated along with its origin identification. In each case, plant height (H) was measured from the pseudostem base on the ground to the vertex formed by the union of the last pair of leaves, using a rigid measuring tape with 1 mm accuracy, as well as the pseudostem perimeter (P50) from the base on the ground, by means of a flexible tape measure with 1 mm accuracy. When the plant was less than 50 cm in total height, no records were taken [23]. Moreover, the phenological stage of each plant in the PU was recorded according to its morphological aspects [24,25]. The classification was as follows: (a) vegetative dependent, plants before F10, leaves narrower than 10 cm, not yet photosynthetically active; (b) vegetative independent, plants after F10 and before apical meristem differentiation, photosynthetically active plants; (c) reproductive, plants after apical meristem differentiation, inflorescence already developed, emerging through the pseudostem; (d) productive, plants that had already developed inflorescence.

Subsequently, a foliar analysis was conducted while following the procedure described by [20]. For leaf no. 3, the following composition was determined: copper (Cu), iron (Fe), manganese (Mn), Zn, boron (B; mg/kg), Ca, magnesium (Mg), potassium (K), and sodium (Na) (%). Samples were taken at the onset of flowering (beginning of the productive stage), on the first week after the emergence of the inflorescence (bunch) and prior to bract drop, at which time sampling for soil analysis was also carried out. For said analysis, a 500 g sample was taken from the PU when the inflorescence emerged apically, following the methodology by [26]. Samples were taken when flowering coincided with the sampling week. In this analysis, the following physicochemical properties were determined: including texture sand (A), silt (L), clay (AR), pH, organic matter, aluminum (Al), Ca, Mg, K, Na, cation exchange capacity (CEC), phosphorus (P), sulfur (S), Fe, Mn, Cu, Zn, and B.

Table 1. Description of soil physical and chemical properties on the California (C-loam class) and San Bartolo (SB-clay class) farms dedicated to banana production for export.

Properties	C	SB	Properties	SB	C
A%	44.20	17.30	Na ppm	1.73	2.17
Ar%	22.20	50.40	CEC meq/100 g	12.17	33.39
pH	6.01	5.03	Cu mg/kg	0.31	0.02
MO%	1.48	1.88	Fe mg/kg	0.17	0.08
Ca ppm	10.35	27.51	Mn mg/kg	0.36	0.63
Mg ppm	1.54	5.45	Zn mg/kg	0.04	0.04
K ppm	0.28	0.43	B mg/kg	0.43	0.36

presents the results of the soil analysis, averaged from 18 samples taken from California farm and 24 from San Bartolo.

Another was associated with the bunches during postharvest, which were harvested ten weeks after emergence. The weight of each bunch (kg) was recorded, as well as the number of hands, and the location where fruits with MB were found. Figure 1 (left) shows a bunch with six hands, which exhibits lesions caused by this physiological disorder on hand nos. 1, 2, and 3, as well as fruits with the typical bronzing generated by MB (right).

To relate the presence or absence (P/A) of MB to certain morphometric variables, this study used the cross-sectional area of the pseudostem (SA50) at 50 cm from the base on the ground, with which the total leaf area of the plant was estimated, according to the model proposed by [23]. Finally, climatic data corresponding to the areas surrounding the studied farms were recorded. This information was compiled from two automatic meteorological stations belonging to the Institute for Hydrology, Meteorology, and Environmental Studies (IDEAM), namely, Chigorodó station (code 12015110) for San Bartolo farm and Paldema station (code 29065020) for California farm. During this recording process, daily data were captured, which included the total precipitation for the day (mm), and the maximum air temperature (°C) at a 2 m height.

2.3. Statistical Analysis

First, a descriptive analysis of each plot was conducted to explore the possible association between the described variables, separating them based on the P/A of MB per farm. In addition, the average weight of the bunches with and without MB in each of the plots was calculated. Statistics for the nutrient concentration in the foliar tissue were also calculated, which turned out to be of interest during modeling. As for soil analysis, only the laboratory results were described. A multiple logistic regression model [27] was employed to analyze the relationship between the response variable (P/A of MB) and different sets of explanatory variables. Although, in principle, independent models were developed for morphometric and production variables for each farm, as well as a global model for the foliar content of the most relevant mineral elements associated with MB formation, the presence of correlations between morphometric variables and high correlations between mineral contents did not allow all these to be included in the final models. Therefore, only those whose correlation did not exceed 0.3 in the case of mineral contents were presented, and only the perimeter (P50) remained as an explanatory variable, which made it possible to unify the two farms in a single data matrix and use the factor associated with the farm as an explanatory variable.

Considering the set of p explanatory variables denoted by the transposed vector $x$^T = ($x$₁, $x$₂, …, $x$_p), and assuming the conditional probability of the response being present (presence of MB) is Pr(Y = 1| $x$) = π($x$), the logit of the multiple logistic regression is given by Equation (1):

$$g\left(x\right)=\ln \left({\displaystyle \frac{\mathsf{\pi }\left(x\right)}{1-\mathsf{\pi }\left(x\right)}}\right)={\beta }_0+{\beta }_1{x}_1+{\beta }_2{x}_2+\cdots +{\beta }_p{x}_p$$

(1)

where ${\beta }_0,{\beta }_1,\dots ,{\beta }_p$ represent the coefficients associated with each explanatory variable in $x$.

The logistic model considers the following epidemiological framework: the variables in $\mathit{x}$ have been observed in the studied PUs, for which the state of the physiological disorder is determined as Y = 1 if there is presence of MB; conversely, the value is 0 in the absence of MB. The modeled probability is expressed via a conditional probability statement: Pr(Y = 1| $x$) = π($x$).

To facilitate the interpretation of the model’s results, odds and odds ratios (OR) were introduced, allowing for a quantitative evaluation of the effect of each explanatory variable. In particularly, the odds ratio of the response in $x_1=1$ with respect to $x_1=0$ (thus comparing both farms) was estimated while keeping the remaining model variables constant. This was achieved through Equations (2)–(4), by making

$$Odds\left(SB\right)=exp[\left({\beta }_0+{\beta }_2\right)+({\beta }_1+{\beta }_3\left)x_1\right]$$

(2)

$$Odds\left(C\right)=exp({\beta }_0+{\beta }_1{x}_1)$$

(3)

$${OR}_{SB/C}={\displaystyle \frac{exp[\left({\beta }_0+{\beta }_2\right)+({\beta }_1+{\beta }_3\left)x_1\right]}{exp({\beta }_0+{\beta }_1{x}_1)}}=\mathrm{e}\mathrm{x}\mathrm{p}({\beta }_2+{\beta }_3{x}_1)$$

(4)

where ${\beta }_0$ represents the coefficient of the intercept, ${\beta }_1$ represents the coefficient associated with P50, ${\beta }_2$ represents the coefficient associated with farm, and ${\beta }_3$ the interaction. The interaction term is multiplicative, so it can be expressed in terms of $x_1$ and $x_2$, which is why it disappears from odds expressions and odds ratio. The odds ratio obtained represents the ratio of the response’s probability of occurrence on California farm in comparison with San Bartolo farm. Based on the probabilistic predictions generated by the model, graphs were constructed that described the variation in the response probability as a function of different scenarios. These scenarios were selected while considering variables that met the model’s assumptions of linearity and fit. Initially, a specific model for the morphometric and production variables was adjusted, as this database was independent of that corresponding to the mineral element content in the foliar tissue. In this first model, the analysis was carried out involving the two farms and the lots involved, as well as the morphometric variable associated with the P50 and its interaction with the perimeter.

In the subsequent modeling phase, using data on the mineral elements in the tissue, the model’s stability (i.e., the suitability of the fit and the statistical effects) allowed grouping of both farms, although, in the end, this effect was left out of the model. The variables selected for this model were the concentrations of Zn and B in the foliar tissue.

The analyses were conducted using R software (version 4.5, released on 11 May 2025) [28]. Initially, purely additive structures were adjusted in all models, followed by models incorporating interactions between covariables. This allowed evaluation of the combined effect of the variables and optimization of the model’s fit. The goodness of fit was determined using the Hosmer–Lemeshow test [29].

3. Results

Next, the results regarding the variables with most explanatory value in the formation of MB are presented, addressing both their descriptive characterization and their inferential component. We incorporated information regarding morphometry, production, the mineral elements in the foliar tissue, and the climate in the regions where the two studied farms were located.

Table 2. Distribution of the average, standard deviation (in parentheses), number of observations (n) for each farm with regard to the P/A of MB in banana bunches (Musa AAA).

Responses	California		San Bartolo
	Absent (n = 217)	Present (n = 114)	Absent (n = 177)	Present (n = 112)
H	305.0 (30.4)	278.2 (23.2)	356.0 (30.0)	324.0 (32.7)
P50	77.2 (5.4)	69.8 (5.6)	76.4 (5.5)	67.6 (5.9)
SA50	474.0 (67.2)	381.0 (62.6)	467.0 (66.2)	366.0 (62.8)
LA50	21.3 (4.2)	15.6 (3.7)	20.7 (4.0)	14.7 (3.6)
WB	25.7 (3.9)	24.1 (3.0)	24.7 (2.4)	23.6 (2.4)

summarizes the descriptive statistics of the variables that were later included in the model, some of which were discarded due to high correlation between the explanatory variables, leaving only those presented in the modeling results. These variables were the height of the main plant (mother plant, H), the pseudostem perimeter at 50 cm (P50), the SA50, the estimated leaf area (LA50) [23], and the weight of bunch (WB). The results presented in this section describe the associations between these variables and the occurrence of MB, providing a statistical basis for the interpretation of the logistic regression model.

As shown Table 2, the ratios calculated for the P/A of MB were greater than 1 in both farms (for example, California: 305.0/278.0). This pattern indicates that, on average, the evaluated variables (H, P50, SA50, LA50, WB) tended to present lower values in plants with the presence of MB compared to those in which the disorder was absent.

Table 3. Average weight of the fruits affected by MB in each plot of the California and San Bartolo farms.

Farm (Plot)	Average (kg)	Farm Average
California-1 (C1)	0.775	0.643
California-2 (C2)	0.511
San Bartolo-1 (SB1)	0.243	0.169
San Bartolo-2 (SB2)	0.094

presents the average weight values for the fruits affected by MB in each plot of the California and San Bartolo farms. Only the weight of the rejected fruits is reported, i.e., those that could not be commercialized due to peel bronzing, without considering the total WB.

Table 4. Average, standard deviation (in parentheses), and number of observations (n) for the nutritional contents in the foliar tissues of banana bunches (Musa AAA) featuring the P/A of MB on each studied farm.

Responses	California		San Bartolo
	Absent (n = 17)	Present (n = 15)	Absent (n = 16)	Present (n = 14)
Ca	1.1 (0.2)	1.0 (0.3)	1.1 (0.2)	1.1 (0.3)
Mg	0.6 (0.3)	0.6 (0.1)	0.7 (0.2)	0.6 (0.1)
Zn	26.1 (4.1)	24.6 (5.3)	26.0 (3.6)	21.5 (3.4)
B	94.0 (34.9)	66.1 (11.4)	89.2 (29.4)	67.5 (17.7)

presents the distribution of the foliar contents in the same groups analyzed in Table 2. As with the previously reported morphometric variables, the quotients of the concentrations of Ca, Mg, Zn, and B in the foliar tissue were higher than 1 in both farms, which suggests an association between the formation of MB and a lower concentration of these elements, among which B exhibited the most significant variation in both farms, at approximate ratios of 1.4 in California and 1.2 in San Bartolo. These results indicate that the most pronounced changes and the direct response in the manifestation of the disorder could be closely related to the concentration of this element in the foliar tissue.

Table 5. Results of adjusting the logistic regression model using morphometric (P50) and production (WB).

Coefficient	Estimate	Standard Error	Z-Value	Pr(>|z|)
Intercept	21.47	2.54	8.45	2.00 × 10−16
P50	−0.30	0.04	−8.63	2.00 × 10−16
SB	−2.01	3.48	−0.58	5.64 × 10−2
P50 × SB	−3.82 × 10−4	5.01 × 10−5	−7.638	2.21 × 10−14

presents the results of the modeling after the selection of variables that made it possible to validate the suitability of the model. The factor used as the reference level was associated with the California farm, but the table only shows the level associated with San Bartolo. Of the morphometric variables, only the one associated with the P50 remained in the final model, as the others mentioned in Table 2 were highly correlated with this, and to avoid the instability of the multicollinearity estimate, the one that generated the best fit model with the statistics presented after modeling was left behind. The interaction effect between the P50 covariate and the farm was also included. The data provide evidence of zero coefficients for interaction and the farm factor; however, its proximity to the usual threshold of 5% made us leave it because even its deletion from the model generated statistics of less appropriate adjustments. In this sense, only P50 rejected the hypothesis associated with its zero coefficient, so a greater relationship with the MB under the conditions evaluated is attributed to this variable.

The models adjusted for each farm did not exhibit overdispersion, as indicated by the relationship between the residual deviation and degrees of freedom (540.4/616.00 = 0.88). The Hosmer–Lemeshow test supported the hypothesis of a good model fit (p = 0.06; eight degrees of freedom) [30]. Based on these results, the probabilities of each observation were estimated, and dispersion diagrams for the explanatory variables were generated, along with the probability of MB P/A.

Figure 2 presents the results obtained from modeling, showing the highest probabilities for the formation of MB to be associated with reduced values regarding P50 in this case. The values are separated by farm only to differentiate with more precision the subtle difference in the perimeter reached when the bronzing is presented. According to the figure, the presence of the stain is a little more evident on the California farm for perimeters below 75 cm, since in San Bartolo, below this perimeter are some cases of absence of the tan associated with MB.

The results of the nutritional content modeling, which used an independent database, are presented below. Although multiple variables were evaluated (as listed in the Methodology section), we only report those that allowed for the model with good fit and whose coefficients and significance level were relevant given their potential contribution to new findings. In this analysis, the farms were not differentiated, and the data were processed jointly, initially including the farm factor. However, this factor was excluded from the model, since no statistical evidence was found that suggested meaningful differences (

Table 6. Results of adjusting the multiple logistic regression model for the concentrations of Zn and B in banana (Musa AAA) foliar tissue.

Coefficient	Estimate	Standard Error	Z-Value	Pr(>|z|)
Intercept	6.71	2.13	3.42	1.66 × 10−3
Zn	−0.04	0.02	−2.82	4.78 × 10−3
B	−0.14	0.07	−1.98	4.80 × 10−2

).

In Table 6, only the coefficients corresponding to B and Zn provided sufficient statistical evidence to be considered non-null, which indicates that these elements could explain, under the evaluated conditions, the P/A of MB. In addition, the results showed no overdispersion, as the relationship between the residual deviance and the degrees of freedom was 66.734/59 = 1.13. The Hosmer–Lemeshow test supported the hypothesis of an adequate model fit (p = 0.8404; eight degrees of freedom). Two mineral elements essential for cultivation were included, which, from a modeling perspective, fulfilled the assumptions required for the application of this approach.

Figure 3 illustrates the relationship between the P/A of MB and the probabilities estimated based on the model. In the case of B (Figure 3A), MB formation is associated with foliar concentrations below 100 mg/kg, with probabilities exceeding 25% (approximately) when the values drop under 80 mg/kg. As for Zn (Figure 3B), the disorder is observed within the 15–35 mg/kg range, but with a higher positive case concentration at Zn values below 25 mg/kg.

In logistic regression models, the OR construction allows a comparison of different scenarios. In this context, based on the adjusted model of each farm, the OR for the P/A of MB was calculated while following a procedure similar to that described in Equation (2). For this purpose, the $M\left(\mathit{x}\right)$ model was defined, which generated the results in Table 5 by considering the vector $=\left[\begin{array}{ccc}1& x_1& \begin{array}{cc}{x}_2& x_{12}\end{array}\end{array}\right]\prime$. The model is expressed in Equation (5):

$$M\left(\mathit{x}\right):\ln \left({\displaystyle \frac{\mathsf{\pi }\left(\mathit{x}\right)}{1-\mathsf{\pi }\left(\mathit{x}\right)}}\right)=[\begin{array}{ccc}21.47& -0.30& \begin{array}{cc}-2.01& 0.03\end{array}\end{array}]\left[\begin{array}{c}1\\ x_1\\ \begin{array}{c}{x}_2\\ x_{12}\end{array}\end{array}\right]$$

(5)

Table 7. Distribution of odds ratios and probabilities by farm.

P50 (cm)	San Bartolo Farm			California Farm			Comparison
	Odds	Prob	Ratio (P/A)	Odds	Prob	Ratio (P/A)	OR (SB/C)	Interpretation
50	387.610	0.997	1:0	645.484	0.998	1:0	0.600	40% ↓ risk-MB
55	100.484	0.990	1:0	144.027	0.993	1:0	0.697	30% ↓ risk-MB
60	26.050	0.963	1:0	32.137	0.970	1:0	0.811	19% ↓ risk-MB
65	6.753	0.871	1:0.1	7.171	0.878	1:0.1	0.942	6% ↓ risk-MB
70	1.751	0.636	1:0.6	1.600	0.615	1:0.6	1.094	9% ↑ risk-MB
75	0.454	0.312	1:2.2	0.357	0.263	1:2.8	1.271	27% ↑ risk-MB
80	0.118	0.105	1:8.5	0.080	0.074	1:12.6	0.477	48% ↑ risk-MB
85	0.031	0.030	1:32.8	0.018	0.017	1:56.3	0.716	72% ↑ risk-MB
90	0.008	0.008	1:126.5	0.004	0.004	1:252.1	1.994	99% ↑ risk-MB

Odds: Presence/Absence; Relation: 1:Odds; Prob: Odds/(1 + Odds); Note: ↓ indicates lower risk; ↑ indicates higher risk.

presents the estimated probabilities of MB occurrence in each farm, together with the presence/absence ratio and the odds ratio between San Bartolo and California. It also includes an interpretation of how these probabilities vary as the pseudostem perimeter increases, highlighting differences between farms. As for the odds ratio, it provides a comparative measure of MB occurrence between San Bartolo and California.

Table 7 presents the estimated probabilities of MB occurrence for each farm, the presence/absence ratio, and the odds ratio between San Bartolo and California. The results show that the greatest variation in risk between farms is observed at pseudostem P50 in the range of 65–70 cm. For smaller perimeters (<70 cm), the probability of MB is consistently higher, with a slightly greater risk in San Bartolo compared to California. These findings indicate an inverse relationship, where a reduction in pseudostem perimeter increases the probability of MB occurrence.

Table 8. Climatic conditions (precipitation, and temperature) on the California (Zona Bananera, Magdalena) and San Bartolo (Chigorodó, Antioquia) farms during 2018 and 2019.

Year	Period	Farm	Precipitation (mm)	Temperature (°C)
2018	November–May	California	98.5 (60.0–117.0)	31.2 (30.5–32.0)
		San Bartolo	2.5 (0.0–162.5)	27.8 (21.4–34.7)
	June–October	California	1.5 (1.0–2.0)	31.0 (30.9–32.0)
		San Bartolo	37.9 (0.0–148.6)	24.0 (27.5–29.2)
2019	November–May	California	33.5 (19.0–192.3)	33.6 (32.2–34.6)
		San Bartolo	144.4 (0.0–193.6)	27.0 (21.9–31.7)
	June–October	California	132.3 (11.6–229.7)	33.5 (33.2–33.9)
		San Bartolo	188.3 (0.0–296.0)	26.0 (21.1–30.5)

presents the behavior of the analyzed climatic variables for the years 2018 and 2019. This includes the precipitation mean and range (minimum/maximum values in parentheses) and temperature, recorded at the hydrometeorological stations of IDEAM that were closest to the farms.

The data in Table 8 evidence differences in the precipitation and temperature conditions of the California and San Bartolo farms between 2018 and 2019. On California farm, between November and May 2018, the mean precipitation was 138.5 mm, whereas, between June and October, this value decreased to 1.5 mm. Temperatures remained high in both periods, with mean values of 31 °C. In contrast, on San Bartolo farm, the mean precipitation for 2018 was lower than that on California farm. The temperatures were moderate, with means of 27.8 °C in the first period and 24 °C in the second one. During the last period of 2018 and the first one of 2019, the precipitation on California farm continued to decrease, while the temperature remained at a mean close to 33.5 °C. On San Bartolo, the mean precipitation was higher, reaching 144.4 and 188.7 mm, with temperatures lower than those observed on California farm, oscillating between 27.0 and 26.0 °C.

4. Discussion

Regarding the morphometric variable, Figure 2 shows that as the pseudostem perimeter increases, the probability of MB occurrence decreases. Among the descriptors evaluated, the pseudostem perimeter appears to be the most influential, as suggested by the modeling results (Table 2). This parameter reflects the structural development of the plant, since a larger perimeter results from the overlapping of the leaf sheaths that form the pseudostem [31]. In practical terms, a thicker pseudostem could be associated with a greater leaf area, better mechanical stability, and a more efficient distribution of resources. This growth pattern continues until the plant reaches its maximum height with the apical emission of the inflorescence [17,25,32,33]. Thus, the pseudostem perimeter reflects plant vigor and is possibly the morphometric factor most closely related to the occurrence of MB.

A smaller pseudostem perimeter may be associated with a reduced leaf area, which can limit transpiration and nutrient uptake by decreasing the capture of photosynthetically active radiation and photosynthesis [34,35]. This restriction could also affect mineral transport from the roots to the upper organs [17,22,25,30], thereby influencing the distribution of nutrients and photoassimilates. In plantain cv. Hartón (Musa AAB), a positive linear relationship has been reported between yield and leaf number [36], suggesting that leaf morphology plays an important role in assimilation efficiency.

The results in Table 7 indicate that as the pseudostem perimeter decreases, the probability of MB occurrence increases, evidencing an inverse relationship between these variables. This trend suggests that reduced vegetative development may be associated with greater susceptibility to the disorder. Moreover, differences were observed between the evaluated farms, which could be related to variations in management practices or soil conditions that modulate the incidence of MB [13].

From an agronomic perspective, an increase in pseudostem perimeter may serve as an indicator of greater plant vigor, associated with a lower probability of maturity bronzing occurrence (Figure 2). The efficient transport of nutrients such as Ca and B is essential for maintaining the nutritional balance of banana plants, favoring their growth and development, and reducing the incidence of physiological disorders [8,9,12,14,17,18,19]. This parameter is directly associated with the transport of photoassimilates and water through the plant [34,35,37], which influences the adequate partition of assimilates during fruit formation [38]. Under limited conditions, this may lead to changes in epicuticular morphology, causing cell membranes to collapse, with the subsequent oxidation of intracellular fluids, giving rise to maturity bronzing [14,39,40].

Although bunch weight (WB) was not included in the predictive models, it remains relevant from a productive standpoint, as lower-weight bunches have been associated with higher incidence and severity of MM [41]. In the obtained results, WB mainly appears as a descriptive trait (Table 2), related to plant vigor. Therefore, the focus on the relationship between WB and the incidence of this physiological disorder could be reoriented toward the selection of banana plants with a higher capacity in their storage organs, as well as toward a more efficient translocation of photoassimilates and the improvement of the radical system’s health and the soil’s nutrient absorption [42]. These characteristics would improve plant resistance, which could in turn influence the formation of the physiological disorder studied.

As for the foliar concentration of B, Figure 3A presents the distribution of the probabilities estimated by the model for the P/A of MB as a function of the changes in the concentration of said nutrient. When the foliar concentration of B is below 100 mg/kg, most cases with MB are observed, with a probability greater than 0.25 that the disorder will occur. When considering a higher probability threshold (e.g., higher than 0.5), the concentration of B descends to 70 mg/kg, suggesting that an association between foliar B levels under 100 mg/kg increases the probability of MB. By contrast, as the concentration of B approaches or surpasses the sufficiency levels, according to [43], the probability of this physiological disorder’s development appears to be associated with a lower incidence. This behavior can be attributed to the essential role played by B in key stages of plant development, particularly during the apical meristem’s transition from the vegetative to the reproductive stage, the formation and initial growth of the fruit, and the transition toward the reproductive phase [22,25,37,43,44].

B plays an essential role in cell division and formation, in cell wall stabilization, and in maintaining the structural integrity of tissues. Furthermore, it is necessary for the correct functioning of the plasma membrane and the incorporation of components such as lignin and hemicellulose into the cell walls. Therefore, B deficiency has been associated with increases in cellular permeability, compromising membrane stability and favoring the degradation of cell structures [19,22,45,46,47], conditions that may be related to the occurrence of the physiological disorder observed in the fruit. This suggests that reduced concentrations of B in the foliar tissue may negatively affect the cohesion of epidermal cells, which could favor the intracellular cracking of the banana peel, thus evidencing the characteristic symptom of the damage in fruits with MB, as described by [14,15]. B also influences the oxidation of phenols, a type of compound that may affect the fruit and contribute to aging and deterioration processes, so an adequate supply of B could be associated with mitigation of these negative effects, thereby ensuring quality [32,45,48]. The above suggests that managing nutrition with B during the critical stages of fruit development, such as meristem differentiation [25], may contribute to lowering the prevalence and incidence of MB, through its association with fruit development processes [19]. In plantains (Musa AAB), it has been documented that reductions in the availability of B significantly affect fruit filling, causing malformations in the fingers, premature ripening, and size reduction, which negatively impact quality and productivity [49]. More recent research on Berangan bananas (Musa AA) has demonstrated that the foliar application of B to the bunch, both at the opening of the last hands and at 30 days, increases the WB and improves the yield [50]. A similar effect has been reported in apples after the application of Ca and B during the first five to six weeks [45], which is attributed to the role of B in the absorption and mobility of Ca [14,19,22,44]. The application of soluble Ca could help to reduce the incidence of MB, as suggested by previous banana studies [11,33,51,52].

Figure 3B presents the relationship between the probability of the P/A of MB and the concentration of Zn in the foliar tissue. Although the response is not as evident as in the case of B (Figure 3A), it was observed that most of the bunches affected by MB exhibited lower concentrations of foliar Zn, specifically when this value was below 25 mg/kg. Here, the probability of MB appearance was higher than 0.5. This observation suggests that Zn concentrations below this threshold may be associated with a greater likelihood of MB. It should be highlighted that the 21–35 mg/kg range is considered high according to the classification established by [53]. The relationship between Zn and MB reduction could be attributed to the key role of this element in photosynthesis, in carbohydrate synthesis, and in the conversion of sugars into starch, as well as in the biosynthesis of tryptophan, a precursor of auxins (AIA), i.e., the hormones regulating cell division and elongation, which influences the size, shape, and overall development of the fruit [19,35,54]. In this context, Zn deficiencies might compromise bunch growth and be linked to morphological abnormalities [43,53,55,56].

The results described regarding the effects of B and Zn in the occurrence of MB must be interpreted while considering the high positive correlation between both nutrients in bananas [5,57]. The evidence suggests that the combined supplementation of Zn and B in commercial production systems has been associated with improved yields [57]. In Figure 3 it was observed that, when the foliar concentration of these nutrients surpasses the adequate threshold and falls within a high range [22,53], the prevalence of MB tends to be lower, which may reflect an association with an optimal nutritional threshold. Our results represent the first scientific report indicating an association between the occurrence of MB and low foliar concentrations of B and Zn.

The differences in the climatic factors presented in Table 8, such as the higher precipitation and moderate temperatures of the San Bartolo farm (Antioquia), may be associated with the amount of fruit lost due to MB. This is reflected in the comparison of scenarios based on the OR, which indicate that the disorder appears more likely in California farm (Magdalena) than San Bartolo farm. These results coincide with those of diverse authors who have observed a correlation between low water availability and high temperatures with the occurrence of MB [10,11,12,13,14,16]. Climatic variables were preliminarily analyzed; however, under the conditions of this study, they did not show statistically significant effects on MB occurrence and were therefore excluded from the model.

5. Conclusions

The findings of this research constitute the first scientific report suggesting a relationship between maturity bronzing in bananas (Musa AAA) and multiple factors simultaneously. The occurrence of this physiological disorder in cv. Williams (Cavendish subgroup) was associated with morphometric parameters and foliar tissue nutrient concentrations, These findings should be regarded as initial references derived from an observational study conducted at two farms in Colombia (California, Magdalena; and San Bartolo, Antioquia), located in contrasting environmental conditions, rather than as definitive management recommendations.

The proposed thresholds for the identified morphometric and nutritional parameters are a pseudostem perimeter below 70 cm (measured 50 cm above the base) and foliar concentrations of B and Zn not exceeding 100 and 25 mg/kg, respectively. These values should be interpreted as preliminary associations that require validation across diverse contexts and genotypes before more robust management thresholds can be established.