The diurnal cycle served to determine the exact time in which the papaya crop has the highest assimilation of CO₂ to take the corresponding variables. With the help of the CIRAS-3 Portable Photosynthesis System, it was determined that at 13:40 h the plants showed greater assimilation of CO₂. For this reason, it was determined to take the measurements of the physiological variables on day 66 (November-17), 96 (December-17), 96 (December-17), 127 (January-18), 158 (February-18), and day 226 after transplanting (May-18) (Fig. 1).

According to statistical analyses, the production system factor registered substantial differences in November sampling. Similarly, the genotype factor proved to have significant changes in November, January and May samplings. Cover crop plants did not register significant differences for any of the samples tested, therefore; there was no interaction by the factors in the samplings carried out. Whereas treatments (product of factors´combination under study) (Tab. 2) had important variances in both November sampling (semi conventional-Maradona-Canavalia) and January sampling (semi conventional-Maradona-vegetative cover).

The behavior of CO₂ assimilation in each factors’ samplings can be seen in Figs. 2A–2C. In contrast to the highest assimilation of CO₂ obtained in February, Tendency indicates that lower assimilation of CO₂ occurred in November. Furthermore, it can be seen that lower values belong to those months wherein day length is shorter and days are longer; in addition, months’ temperatures correspond to the highest and lowest temperatures. Such situation may be the cause of said behavior.

In other studies conducted under different conditions, in which a conventional production system with diverse genotypes had been used, Campostrini et al. [12] and Wang et al. [13] report higher values than those registered in this study, but de Lima et al. [14] report inferior assimilation rates to the current investigation; proving genotypes have dissimilar behavior when they are under a specific production system, as demonstrated by de Castro et al. [15]. They suggest genotypes have a significant variability for photosynthesis because of their adaptation to the environment.

Because of the close connection with CO₂ assimilation and transpiration in most samples, correlations in CO₂ assimilation with the variables presented positive correlation with stomatal conductance (Tab. 3). In that regard, Díaz et al. [16] state that stomatal conductance estimates the gas exchange rate and transpiration. In February, there was no relation with the stomatal conductance but there was with intercellular CO₂. There was also some higher assimilation of CO₂ regardless of the factors and treatments. This coincides to what Zhou et al. [17] suggested at low concentrations of intercellular CO₂, the assimilation of CO₂ rises. In conclusion, this analysis for positive correlations indicates that values for transpiration, water use efficiency, stomatal conductance, and intercellular CO₂ (dependent variables) tend to increment as the assimilation of CO₂ (independent variable) rises.

ANOVA under an experimental design of subdivided parcels presented significant differences in February 2018 production systems. Genotypes, for its part, revealed major variances that were highly significant in November and May. The cover crop factor also showed a highly significant variance in February and May. The interaction of factor production system*, coverage showed some significance in November sampling, but highly significant evidence was exposed in May sampling. However, the *genotype* coverage interaction displayed some significance in February sampling, but highly significant evidence was presented in the latter sampling for the production system *genotype* coverage interaction, (Tab. 2). In factors’ combinations, November sampling presented significant differences with semi conventional-Maradona-Canavalia (8.11 mmol m^-2 s^-1); whereas in February with semi conventional-Maradol-vegetative cover (8.25 mmol m^-2 s^-1) and in May with semi conventional-Maradona-no cover (9.12 mmol m^-2 s^-1) were highly significant.

Figs. 3A–3C revealed the dynamics in transpiration during months’ sampling. Within the three factors, the tendency indicates there was low transpiration in January due to decreasing temperatures, in which case February showed the highest temperature.

In studies handling different genotypes and systems, Wang et al. [13] and de Lima et al. [14] reported a transpiration of 7.80 mmol m^-2 s^-1 and 5.5 mmol m^-2 s^-1. These values are similar to those gathered in the current study using the production system and genotype factors. Synthetic cushioning influences transpiration directly, as values of 25.13 mmol m^-2 s^-1 for red cushioning and 24.87 mmol m^-2 s^-1 for black cushioning were shown in melon crops Zhou et al. [17]. This clearly indicates that the use of synthetic covers induces greater transpiration, while vegetative covers diminish the transpiration rate.

Correlation in transpiration with other variables can be observed in Tab. 4. It shows some positive correlation with stomatal conductance indicating their close connection, as it was previously mentioned in the CO₂ assimilation section. Hence, these positive correlations point out that values for stomatal conductance and intercellular CO₂ (dependent variables) tend to rise as transpiration (independent variable) increases. But as transpiration grows, the value for water use efficiency diminishes.

ANOVA presents some highly significant evidence (0.005** P ≤ 0.05, Tukey) in the cover crop factor during January and February samplings, but for production system and genotypes factors, no significant variances were registered during these months’ samplings. Regarding the interaction of factors’ production system*, genotype exhibited a significant effect in February samplings; however, for the production system * coverage interaction, very significant evidence was shown in January sampling. In addition to genotype*, the interaction coverage showed some significance in February. Likewise the interaction production system, * genotype * coverage showed some significance in January sampling, but for December, highly significant evidence was presented (Tab. 2). In treatments, however, there was a significant difference in December sampling with organic-Maradona-no cover (4.90 g fixed CO₂ * kg^-1 transpiration of H₂O), but major differences both in January (semi conventional-Maradona-vegetative cover 3.02 g fixed CO₂ * kg transpiration of H₂O) and in February (organic-Maradol-Canavalia 3.77 g fixed CO₂ * kg transpiration of H₂O).

The dynamics in water use efficiency can be observed in Figs. 4A–4C. The best water use efficiency occurred in November and December, whereas it was less efficient in February and May because temperatures rose considerably. Zhou et al. [17] state that due to a less demand in evaporation, there is greater water efficiency in low temperatures. But in high temperatures, there is less water efficiency. This does not suggest that environmental factors and the plant itself influence water efficiency; on the contrary, atmospheric humidity is the most predominant factor that influences it [17].

Tab. 5 displays the correlations in water use efficiency. Some negative correlations with most variables except with chlorophyll can be observed, indicating that environmental and physiological factors would make water use to be less efficient in the plant. Alcántara et al. [18] suggest that transpiration efficiency with concentrations of CO₂ have a negative relation in leaves, as it can be observed in November sampling. Negative correlations, however, indicate that values for stomatal conductance and intercellular CO₂ (dependent variables) decrease as water use efficiency (independent variable) increases.

Production systems presented significant differences (Significant evidence 0.015* P ≤ 0.05, Tukey) in November sampling, which were highly significant in January. Among genotypes, important variances in both December and January were registered, which were also highly significant in May. Cover crops shown some significant changes in May. In November sampling, genotype displayed some significance in the interaction of production system, in addition the interaction of production system* genotype coverage exposed greatly significant evidence (Tab. 2). Within treatments, there were important differences in November with semi conventional-Maradona-Canavalia (344.33 mmoles m^-2 s^-1), and in May with organic-Maradona-vegetative cover and semi conventional-Maradona-no cover (389.33-390.00 mmoles m^-2 s^-1), respectively. Highly significant differences were also registered.

Figs. 5A–5C shown stomatal conductance within months’ samplings. Factors behaved in the same manner to which low stomatal conductance can be observed in January, while in February high conductance was registered. As previously mentioned, this effect occurred due to low temperatures noted down in January. In an experiment carried out by Wang et al. [13] reported 180 mmoles m^-2 s^-1 for Tainung No. 2 variety; whereas de Lima et al. [14] registered 210 mmoles m^-2 s^-1 for Grand Golden variety. Consequently, the values listed in this study were low. Campostrini et al. [12] found higher values than the ones in the current study: 400 mmoles m^-2 s^-1 by using Sunrise Solo 72/12 genotype.

Tab. 6 shows the correlations in stomatal conductance with intercellular CO₂ variable as well as the relations with months’ samplings. This coincides with the results reported by Collavino et al. [2], concentrations of intercellular CO₂ control the opening of the stomata in relation to the photosynthetic demand of CO₂ in the plant. As a result, low concentrations of intercellular CO₂ stimulate the opening of the stomata and vice versa. This variable showed some positive correlation with the intercellular CO₂ variable, which indicates that intercellular CO₂ variable has the same tendency as the value of stomatal conductance increments.

Significant differences among genotypes were observed in December and May. In cover crop, significant and major differences were registered in January and February samplings, respectively. The interaction among production system* coverage showed significant evidence in January, while amongst genotype factors* coverage revealed significant evidence in February and some highly significant evidence in January (Tab. 2). Among treatments, important variances were noted with semi conventional-Maradona-vegetative cover (313.00 µmol CO₂ mol⁻¹), however; February proved greater values with organic-Maradona-no cover (286.67 µmol CO₂ mol⁻¹).

Figs. 6A, 6B, and 6C show the behavior of intercellular CO₂, in which concentrations were lower in November and May, whereas February presented higher concentrations of CO₂. According to Alcántara et al. [18] small concentrations of CO₂ in leaves enlarge the opening of the stomata but rising concentrations of CO₂ reduces it. If intercellular CO₂ serves as marker for photosynthesis activity, then the photosynthetic demand for CO₂ is definitely related with the opening of the stomata.

In that regard, Wang et al. [13] found a value of 315 µmol CO₂ mol^-1 for the ‘Tainung No. 2’ variety. This value was superior to the one obtained in this study because diverse genotype and handling had been used.

As a result of the production system, yield exhibited extremely significant differences (Highly significant evidence 0.001* P ≤ 0.05, Tukey), but both genotype and cover crops factors did not display any significant effect in it. No significance was presented by the interaction among factors (Tab. 2), however; treatments did expose greatly significant differences.

Tukey’s multiple comparison determined that the semi conventional system was bigger in yield (43.843 t ha^-1), followed by the organic system (23.66 t ha^-1) with a slightly difference of 53.96%. It was also verified that the semi conventional-Maradona-Canavalia treatment, with a value of 53.45 t ha^-1, had better yield than the other treatments (Tab. 7). Fig. 7 shows yield as consequence of production systems, to which the semi conventional production system excelled in comparison with the organic production system.

In other studies, a yield of 28.60 t ha^-1 by using mineral fertilizers was noted, whereas with organic fertilizer, the yield was of 27.23 t ha^-1 [2]. In response to chemical fertilization, the values gathered for papaya Maradol were of 91.14 t ha^-1 [18]. The statistics of the Agricultural and Fisheries Information Service [1] indicated that the yield in the state of Guerrero was of 38.45 t ha^-1. As previously mentioned, yields registered within the distinct studies and statistics were inferior to the ones gathered in this study.