Diagnosis and management of nutrient constraints in papaya

C H A P T E R

42 Diagnosis and management of nutrient constraints in papaya Ro´ger Fallas-Corralesa,*, Sjoerd E.A.T.M. van der Zeea,b a

Soil Physics and Land Management Group, Wageningen University, Wageningen, The Netherlands b School of Chemistry, Monash University, Melbourne, VIC, Australia *Corresponding author. E-mail: [email protected] O U T L I N E

1 Introduction

607

2 Uses and nutritional value

607

3 Botany

608

4 Habitat

609

5 Growth behavior and management-related aspects

610

6 Diagnosis of nutrient deficiencies 6.1 Morphological diagnosis of nutrient deficiencies 6.2 Leaf nutrient standards and chemical content-based methods

612 612 613

6.3 Complementary methods for diagnosis of nutrient constraints

614

7 Nutrient management 7.1 Nutrient requirement for C. papaya L. 7.2 Management aspects related to soil acidity 7.3 Fertilizer dosage for papaya using an empirical approach 7.4 A mechanistic modeling approach to management

615 615 616

8 Conclusions and perspectives

624

Acknowledgments

625

References

625

618 620

1 Introduction Carica papaya L. is a particularly high yielding tropical crop, originally from Central America and the South of M
exico; its domestication is attributed to the Aztecs and Mayas (Fuentes and Santamaría, 2014). As it originates and is grown in tropical and subtropical regions (C
eccoli et al., 2013), the crop suffers from chill conditions in temperate regions. The crop is now found in different tropical regions around the world. The Caricaceae family, which C. papaya L. is part, is composed of six genus (Carica, Vasconcellea, Horovitzia, Jarilla, Jacaratia, and Cylicomorpha); most of them with an American origin and as shown in Fig. 42.1 can be found growing in wild areas, for example, in Central America. Only one of the genus (Cylicomorpha) is found growing wildly in regions of Africa, even though it is hypothesized that Carica has an African ancestor that traveled by surface sea currents through the Atlantic Ocean and started to colonize in the American continent (Carvalho and Renner, 2014).

2 Uses and nutritional value Nowadays, papaya is used for several purposes, for example, as a fresh fruit (green or ripe), for juice production, jams, and as crystallized fruit and canned (Paull, 1993). Also, more industrialized products such as the production of A.K. Srivastava, Chengxiao Hu (eds.) Fruit Crops: Diagnosis and Management of Nutrient Constraints https://doi.org/10.1016/B978-0-12-818732-6.00042-3

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© 2020 Elsevier Inc. All rights reserved.

608

42. Diagnosis and management of nutrient constraints in papaya

(A)

(B)

FIG. 42.1

Genus of the Caricaceae family (Carica and Vasconcellea, respectively) found in wild areas of Acosta, Costa Rica, at approximately 600 m above sea level. (A) C. papaya L. (B) Vasconcellea fruits.

TABLE 42.1 Average mineral concentrations for the fresh flesh fruit of C. papaya L. from one farm located in the Atlantic Region of Costa Rica. g/100 g flesh

Concentration a

Conf. Interval a

mg/100 flesh

N

P

Ca

Mg

K

S

Fe

Cu

Zn

B

109.40

10.20

17.60

11.90

171.00

6.30

0.28

0.04

0.10

0.15

0.23

0.02

0.05

0.01

0.42

0.02

0.01

0.001

0.003

0.001

Confidence interval alpha ¼ 0.95, N ¼ 10.

latex for the candy industry and the production of the enzyme papain that is used for digestive disorders, treatment of ulcers, and diphtheria are prepared from papaya (Reddy and Dinesh, 2000). Papaya is considered as one of the most nutritional and healthy tropical fruits; it has antioxidant properties derived from the presence of several phenols, flavonoids, carotenoids, and amino acids (Nugroho et al., 2017; Somanah et al., 2018). Papaya also has been used in the preparation of several nutraceutical products, for example, cancer prevention, to reestablish the immunological and metabolic functions in tube-fed patients (Fujita et al., 2017) and to diminish the levels of glucose in type 2 diabetes patients (Danese et al., 2006). The mashed fruit is used for the treatment of infected burns, showing antimicrobial activity and proteolytic activity over necrotic tissue (Starley et al., 1999). Papaya is a good source of minerals and vitamins, which can vary depending on cultivars, management practices, and ripening stage. Wall and Tripathi (2014) compilated the nutritional value of fresh fruit from different sources, and between the most interesting compounds found in papaya, the high contents of vitamin A and C are notable and also the high content of potassium. An example of mineral concentrations for the flesh fruit of papaya cv. Pococí from one farm in the Caribbean region of Costa Rica is highlighted (Table 42.1). The total content of carotenoids varies between cultivars. For instance, the cultivars golden and sunrise solo have a total carotenoid content around 734 and 1013 μg/100 g of FW, respectively (Martins et al., 2016): for maradol considering a 10% of dry matter in the fresh fruit, the carotenoid content is around 3500 μg/100 g of FW (Ovando-Martínez et al., 2018), and for the cultivar Pococí, Schweiggert et al. (2011) reported contents between 5414 and 6214 μg/100 g of FW. However, for a detailed review about the nutritional value of papaya, the reader is referred to Wall and Tripathi (2014).

3 Botany Papaya is a giant herb, and it can reach 9-m height, but in commercial plantations, it is rarely found with more than 5–6 m ( Jim
enez et al., 2014), as in taller plants, agronomical management is difficult. It is a fast-growing crop, normally with a single stem canopy and palmately lobed leaves (Campostrini et al., 2018; Jim
enez et al., 2014; Wang et al., 2014), with a spiral 3:8 phyllotaxis that consists of 3 leaves positioned clockwise or counterclockwise each 360 degrees and

4 Habitat

609 FIG. 42.2 Exemplification of xylem discretization in papaya through the staining of the plant tissue by the application of acid fuchsine dye to a single root. Note that only part of the plant was stained with the (purple color; light gray in print version) of the dye. Photo: R.A. Fallas-Corrales.

required 8 leaves to have a new leave perfectly aligned vertically with the one present in the upper part of the canopy (Campostrini et al., 2018; Fisher, 1980). This phyllotaxis confers to papaya a high radiation use efficiency and gas exchange. Papaya plants can be present in one of three different sex, female, male, or hermaphrodite. For commercial plantations, the hermaphrodite sex is preferred because of the lower internal cavity of the fruit and its elongated shape that can be stored in a smaller volume. The root system in adult plants is composed of a principal tap root of 0.6 m that in some cases may be branched and a set of secondary roots developed on the first 15 cm of depth, which can achieve up to 4-m long in a 1-year-old plant (Fisher, 1980). In some plants, the plant xylem is organized with separate connections, as mentioned by Guti
errez (1997). Papaya is one of those plants, for which each root is connected directly only to a specific part of the plant (Fig. 42.2). This property has direct consequences for the water and nutrient management in the crop as it affects processes like transport of water and nutrients within the plant.

4 Habitat Papaya production is restricted to tropical and frost-free subtropical regions. In subtropical regions, it sometimes is necessary to protect it from chilling in greenhouse structures. The principal problems that papaya faces in subtropical cold regions are the ceasing of crop growth and fruit production due to low temperatures (Allan, 2002). Allan and Biggs (1987) using controlled temperature in greenhouses found that optimal temperature for the crop is in the range of 25–30°C at daytime and between 11°C and 16°C during the night for subtropical regions. They also mention that high-temperature regimes (36/28°C (day/night)) promote the fast growth of the plant, but high temperatures have the disadvantage of other problems, such as low pollen viability and sooner maturation of fruits, resulting in small and poor quality fruits; Allan and Biggs (1987) also mention that, under cool regimes (20/12°C (day/night)), papaya shows slow growth; sex reversal in female plants may occur as well as failure in pollen germination. The optimum temperature for growth according to Jim
enez et al. (2014) is between 21°C and 33°C. Regarding altitude, in tropical conditions, papaya is preferably grown between 0 and 600 m above sea level, where temperatures are more suitable for the crop. As a pioneering plant, papaya prefers habitats with high radiation, and papaya has a high photosynthetic capacity under high radiation regimes. For example, Wang et al. (2014) found net CO2 assimilation rates from 20 to 24.2 μmol/m2/s in fully expanded and fully exposed leaves, and Chutteang et al. (2007) found assimilation rates of 22.5 and 27.9 μmol/m2/s in hermaphrodite and female plants, respectively. The light saturation point of papaya is very high, and Marler and Mickelbart (1998) found values between 1279 and 1325 μmol/m2/s for different varieties grown in field conditions. Papaya prefers habitats with relatively abundant water, as the crop is very sensible to a water deficit; for example, Marler and Mickelbart (1998) report the reductions in 85% of the CO2 assimilation with slight reductions in the soil water tension (more negative values). As will be explained later, papaya can adapt to several soil conditions, but it prefers high-fertility and well-drained soils. Khondaker and Ozawa (2007) mentioned that flooding papaya fields for a period of 48 h may kill the crop.

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42. Diagnosis and management of nutrient constraints in papaya

5 Growth behavior and management-related aspects The commercial cycle of the crop starts with the plantlet production, which stage is characterized by slow growth of the plant and it last between 2.5 and 3.5 months (Bogantes et al., 2011). After being transplanted to the field, the cycle is characterized by a vegetative stage, which lasts up to 2½–3 months after transplantation (MAT). Afterward, a reproductive stage that starts at 2½ or 3 MAT with the flowering and initiation of fruit production follows. This stage continues during all the remaining life cycle of the plant, as this crop continuously produces flowers, fruits, and vegetative structures. The commercial plantation cycle is usually limited to around 2–2.5 years due to the difficulties of management in big plants and decreasing yields as the plants age. However, under unmanaged conditions, there are reports of plants living for up to 20 years (Marler et al., 1994) (see Fig. 42.3A). High yielding in this crop is closely related to efficient water and nutrient management. Regarding water management, Campostrini et al. (2018) reviewed the factors that affect water use efficiency in papaya and also the economic impact of water scarcity. Papaya is considered a drought-tolerant crop, as it can survive to very dry soil conditions; according to Mahouachi et al. (2006), papaya can increase ion concentrations to adjust its osmotic balance. But on the other hand, stomatal conductance and photosynthesis are severely reduced under soil water tensions around 50 and 70 KPa (Lima et al., 2016; Marler and Mickelbart, 1998). It appears that an optimum soil water suction for papaya is around 10 KPa (de Lima et al., 2015); unpublished data for the cultivar Pococí are in accordance with this value. The suction of soil water is usually given as the (negative) pressure. The optimum soil water pressure is therefore 10 kPa (this is a suction of about 100-cm water or in terms of the soil water retention function: pF ¼ 2), which agrees with the pressure at what is generally used as the value of the field capacity (the amount of water that can be held by soil with capillary forces). Exceeding the field capacity leads to drainage of water. The implication is that water availability is optimal at field capacity, which is difficult to maintain: slightly excessive irrigation immediately leads to drainage. In addition, the hydraulic conductivity of soils at about field capacity can be large. For instance, the unsaturated hydraulic conductivity at pF ¼ 2 estimated by the Van Genuchten-Mualem model using the R package “SoilHyP” (Dettmann and Andrews, 2018; R Core Team, 2017), for a loamy sand soil from La Rita, Costa Rica, and a clayey soil from Cariari, Costa Rica, showed values of 0.80 and 1.35 cm/day, respectively, while, in another sandy clay loam, it was in the order of 0.07 cm/day. To give an impression, we show Fig. 42.4 that describes the loss function of water in a root zone at some distance above groundwater. This loss function gives the relationship between the “loss” of root zone water due to evapotranspiration and drainage and the water saturation (s  θθs , with θs being the saturated volumetric water fraction). If the root zone is well above the groundwater level, the loss function is unaffected by capillary rise of water from groundwater

(A)

(B)

(C)

FIG. 42.3 (A) Very tall and old C. papaya L. plant growing spontaneously in an urban area of San Jos
e, Costa Rica. (B) 4½-month-old (MAT) C. papaya L. plant in San Carlos, Costa Rica. (C) 8½-month-old (MAT) C. papaya L. cv. Pococí plantation in La Rita, Costa Rica. (C) Courtesy of Antonio Bogantes and Eric Mora.

5 Growth behavior and management-related aspects

611

FIG. 42.4

Water loss function for a root zone in the absence (RI model) and in the presence (piecewise linear approximation) of groundwater that allows for capillary rise of water. Reproduced from Vervoort, R.W., van der Zee, S.E.A.T.M., 2008. Simulating the effect of capillary flux on the soil water balance in a stochastic ecohydrological framework. Water Resour. Res. 44, W08425. doi:10.1029/2008WR006889.

level into the root zone. In that case, it comprises a linearly increasing part, a horizontal part, and a curvilinear part, going from dry soil (s ¼ 0) to complete saturation. The linearly increasing part represents the dry regime, where evapotranspiration is limited by the amount of water in soil. The horizontal part concerns optimal water availability to meet the evapotranspiration demand (which is controlled by incoming radiation, temperature, and water vapor pressure and the plant type, of course). For papaya, the curvilinear part is the most important. It shows that, as the water content approaches the so-called water field capacity of soil, the loss increases very rapidly with increasing soil wetness: root zone water loss at these saturations is predominantly drainage toward groundwater. The right panel of Fig. 42.4 shows that, if capillary rise can occur, this partly can compensate the evapotranspiration and drainage losses (making loss smaller). This effect is larger as groundwater depth (Z in Fig. 42.4) is smaller. As is well known, the soil water retention function, hence the field capacity, and the hydraulic conductivity depend on the soil type and texture. Accordingly, the precise shape of the loss function is also strongly dependent on soil type (Vervoort and van der Zee, 2008). Campostrini and Glenn (2007) mentioned that papaya responds to irradiance changes with variations in stomatal conductance rates, and this way, under cloudy conditions, the plant reduces transpiration water losses. Additionally, the leaf to air vapor pressure deficit (VPDleaf-air) is especially important for papaya; it has been observed that, when it increases above 1 KPa, there is a reduction in stomatal conductance and, consequently, net photosynthesis assimilation is also reduced (Campostrini et al., 2018). Whereas papaya favors a high soil moisture level for an optimum growth, it does not tolerate waterlogged soil conditions (Campostrini and Glenn, 2007; Khondaker and Ozawa, 2007). If soil becomes so anaerobic that oxygen concentrations in the root zone become too small, papaya is stressed, and its yield is quickly depressed, it reduces its net assimilation A and the stomatal conductance gs (Thani et al., 2016), and it becomes more susceptible to papaya blight disease (Phytophthora palmivora). The need of a sufficiently aerated root zone implies that groundwater levels should not be too shallow and that the soil hydraulic conductivity must be sufficient to drain an excess of water at high saturation. As far as experimental evidence is available, papaya may be considered as moderately sensitive to soil to salinity (Maas, 1993); then, a threshold of 3 dS/m is the maximum electrical conductivity to obtain 100% of the potential yield under otherwise optimum conditions. This factor may be of great importance in systems with application of high rates of fertilizer and low leaching of the nutrients. In summary, the agronomic experience with growing papaya is that it responds favorably to fertilization and good water supply under (sub)tropical climate conditions, but frost, inundation, and other causes of a water saturated soil that lead to poor soil aeration need to be avoided. This may give

612

42. Diagnosis and management of nutrient constraints in papaya

constraints with regard to rainfall and irrigation rates, and these constraints depend on the soil hydraulic properties (water retention and hydraulic conductivity). Though papaya is not highly sensitive to salt levels, the high fertilization rates that are needed to accomplish high yields may be constrained by papaya’s salt tolerance. Moreover, the need to avoid large nutrient leaching losses to groundwater, which may deteriorate the quality of groundwater reserves, also constrains water and fertilizer applications. All these factors render the management of papaya growing a complicated process, with several trade-offs that need to be dealt with. The scope of this chapter is to provide an inventory of the nutrient needs of papaya for the cultivation cases where large yields are aimed at. We particularly consider the perspective of soil fertility management. At this moment, a balance between soil fertility and environmentally sustainable management may not be well possible, due to gaps in knowledge. Therefore, we indicate the possible conflicting interests, and we suggest routes for improvement in this issue.

6 Diagnosis of nutrient deficiencies The correct diagnosis of nutrient deficiencies is a critical task for the improvement of soil fertility and of the fertilization management in crop systems. Such a diagnosis can be done (including for papaya) looking for visible nutrient deficiency symptoms (morphological diagnosis) and by the interpretation of leaf or tissue analysis (leaf nutrient standards and chemical content-based methods).

6.1 Morphological diagnosis of nutrient deficiencies The first mentioned approach has the drawback that the reduction of the yields may already occur when the deficiency symptoms are visible. Still, in the field practice where tissue analysis may not be simple, it is important to recognize deficiency symptoms as early as possible, to appropriately deal with the problem. Cibes and Gaztambide (1978) characterized the nutrient deficiency symptoms in papaya. In their results, nitrogen (N) appeared as the most limiting nutrient for the plant growth, affecting the growth of the complete plant under nitrogen omission. These symptoms have been also described by Thomas et al. (1995) and Kaisar et al. (2013). Kaisar et al. (2013) additionally reported a reduction in the number of fruits per plant and a lower fruit size under nitrogen scarcity. The reduction in the number of fruits per plant when nitrogen is deficient has also been observed by others in experiments where the applied nutrients have been varied (De Brito Neto et al., 2011; Sales Marinho et al., 2001). For phosphorus, Cibes and Gaztambide (1978) concluded that it is the second most limiting nutrient, after nitrogen, for the growth of the vegetative parts of the plant. Nitrogen deficiency causes growth reduction of the entire plant, but only a reduced growth of the aboveground parts occurs in case of P-deficient conditions. Under such conditions, the papaya root system increases in mass, size, and density, as the plant becomes more efficient in extracting soil P. Vichiato et al. (2009) found that P deficiency can be related to lower plant dry matter production; additionally, Kaisar et al. (2013) found that P deficiency induced an earlier starting of the flowering process, reduction in the number of fruits per plant, lower yield, and a decrease in the quality (total soluble solids) of the harvested fruits. Potassium deficiency also induces a reduction of plant growth, and in particular, the stem diameter and height become smaller (Cibes and Gaztambide, 1978; Kaisar et al., 2013). Potassium deficiency has been related to a reduction in the number of fruits per plant and with a lower postharvest quality (reduction in total soluble solids) (Kaisar et al., 2013). Besides its effect on yield and fruit quality, K plays an important role in reducing the frequency of occurrence and severity of some diseases, as documented by Dordas (2008), who also explained the underlying mechanisms. Papaya is not an exception; fruits of the cultivar Pococí submitted to very high potassium rates showed a reduction of the incidence and severity in postharvest diseases compared with normal fertilization (unpublished). Therefore, potassium deficiency symptoms can also be observed at the end of the commercialization process as more prompt to postharvest diseases. Calcium deficiency has been associated with a decrease in the root growth of the plant, with impacts on fruit quality. For instance, Qiu et al. (1995) found postharvest problems (stiffened fruit) when low levels of Ca were present. For Mg and S deficiencies, Cibes and Gaztambide (1978) did not observe reductions in the root and stem growth, but the fruit set was reduced under its deficiency. On the other hand, Kaisar et al. (2013) reported a reduction in plant height as a consequence of S deprivation and agreed with the reduction of the fruit number per plant and its consequent lower yield.

6 Diagnosis of nutrient deficiencies

613

Regarding micronutrients, boron deficiency is maybe the most common and most studied one. In vegetative stages, it is characterized by a reduction of the plant growth (Cibes and Gaztambide, 1978; Singh et al., 2010), reduction of leaf size, limited stem growth, reduced internode distance, and a distortion of the lamina and curvature of petioles (Nautiyal et al., 1986). Boron deficiency has a notorious effect on the fruit set and fruit deformation. Bogantes et al. (2011) showed the effect of boron deficiency in the early stages of fruit development. They describe it as a nonsealed apex of the fruit, which later can be invaded by pathogens that can cause the early drop of the fruit. Boron has been reported as the most limiting nutrient regarding the number of fruits per plant (Kaisar et al., 2013). The most notorious symptom of boron deficiency is the fruit deformation, also known as a bumpy fruit (Wang and Ko, 1975), which is followed by exudation of latex. Using this symptom to diagnose boron deficiencies compromises the production because it is usually too late when detected. Even so, it is important to correct this deficiency because papaya has a continuous fruit production and vegetative tissue over its entire life (starting approximately 3 months after transplanting into the field), and commercial plot cycles normally last around 2 years. Some of the symptoms reported as a boron deficiency in papaya can be confused with symptoms caused by viral diseases. For example, internode distance reduction, fruit deformation, leaf lamina distortion, the curvature of petioles, and fruit latex exudation have been reported as symptoms caused by virus pathogens (Becerra et al., 1999). Due to the aggressiveness of viral diseases in papaya production, it is recommendable to be aware and to distinguish when those symptoms are related to B deficiency or viral diseases. Total boron uptake of the plant may depend on its availability in the soil solution, the demand of the hybrid or variety, and the attainable yield. Cunha and Haag (1980) reported a total B uptake of 74.2 mg per plant using a papaya plant not submitted to a breeding program; on the other hand, Fallas et al. (2014) investigating the nutrient demand of a high yielding hybrid (Pococí) found a total uptake around 190-mg B per plant.

6.2 Leaf nutrient standards and chemical content-based methods A more effective method than the detection of nutrient deficiencies by morphological diagnosis is the use of chemical analysis of plant tissue samples. In this way, nutrient deficiencies can be detected and corrected early, but as it requires a correct interpretation of the results of the analyzed sampled, this approach must first have been developed sufficiently. A commonly used method for the interpretation of tissue analysis is the “sufficiency range method,” which is based on critical concentrations for the crop determined under specific sampling conditions. The accurate interpretation of tissue analysis by conventional approaches as the sufficiency range method is limited by factors related to sampling conditions, plant varieties, and interactions between nutrients and their mobility in the plant. Therefore, for interpretation, it is important to consider the conditions in which the standard sufficiency ranges were developed. A profound complication for tissue analysis interpretation arises when the deficiency is to be assessed in organs like fruits, which is quite common to diagnose for fruit deformations, fruit abortion, low growth and low weight of the fruit, or severe affection by diseases in fruit. At this moment, it appears that there is a lack of reference to compare fruit concentrations in diagnosing in these situations. In addition, it is not established yet if deficiency in petiole or leaf is well correlated with deficiency in the fruit. It is important to emphasize that when a deficiency appears in organs like fruits, the diagnosis might come too late to save this fruit, but it can help to avoid the same problem for the following fruits of the same plant. Nevertheless, plant tissue analysis is a powerful tool for nutrient constraint detection, and if complemented by alternative methods of interpretation, it can give valuable information for the nutrient management in production systems. For papaya, mainly the macronutrients nitrogen (N), phosphorus (P), and potassium (K) restrict the growth and yield. Thus, it is common to detect symptoms of their deficiency by means of visual symptoms and by the use of leaf analysis. However, micronutrient deficiencies can also play an important role in papaya growth and the quality of harvested fruits, and their deficiency also has notorious effects on the plant growth. The sufficiency range standard method for papaya is used with two different plant tissues, that is, leaf and petiole, of which the analysis of petiole is the most commonly used. However, there is still a discussion about the suitability of leaf lamina, or petiole, in the diagnosis of nutrient deficiencies in papaya. Some results point to better predictions of the nutrient status of the plant based on petiole, but others based on leaf lamina (Marinho et al., 2002). The tissue used for the analysis normally corresponds to leaves or petioles from the most recent fully expanded leaf that contains the most recently open flower, which is sometimes called leaf F (Marinho et al., 2002; Mills and Jones, 1996). The appropriate implementation of the nutrient deficiency diagnoses using the sufficiency range method requires to consider (i) a specific tissue, (ii) the season of sampling, (iii) the growth stage, and (iv) a representative number of

614

42. Diagnosis and management of nutrient constraints in papaya

TABLE 42.2

Critical nutrient concentrations (sufficiency ranges) for C. papaya L., as compiled from different investigations. Malavolta et al. (1989) Brazil

Robinson et al. (1997) Australia

Chatterjee and Dube (2004)

Range of concentrations observed in Costa Rica (LSF)a Costa Rica

Reference Country

Mills and Jones (1996) United States

Malavolta et al. (1989) Brazil

N

1.01–2.50

1.0

4.5–5

1.3–2.5

1.01–2.5

0.99–4.60

P

0.22–0.40

0.3

0.5–0.7

0.2–0.4

0.22–0.40

0.11–0.32

K

3.30–5.50

2.5–3.0

2.5–3.0

3.0–6.0

3.3–5.5

2–4.4

Ca

1.00–3.00

1.5

2.0–2.2

1.0–2.5

1.0–3.0

0.68–1.90

Mg

0.40–1.20

0.4

1.0

0.5–1.5

0.40–1.50

0.21–0.40

S

–

–

0.4–0.6

0.3–0.8

0.20–0.40

0.1–0.34

–

–

–

–

–

8.0–39

Na

–

–

–

–

–

160–801

B

20–30

–

15

20–50

20–30

16–25

Cu

4–10

11

4–10

4–10

1.1–5.1

Fe

25–100

–

291

20–80

25–100

16–88

Mn

20–150

–

70

25–150

20–150

15–58

Zn

15–40

–

–

10–30

14–40

9.0–26

Al

%

mg/kg

Sampling season Tissue

Six months after transplant Leaf petioles from the most recent fully expanded leaf

Leaf petioles

Leaf lamina

Leaf petioles from the most recently opened flower

Sixth petiole from the apex

Leaf petioles

a

Range that includes the concentration of the 75% of the samples analyzed in Costa Rica. From Bogantes, A., Mora Newcomer, E., Umaña Rojas, G., Loría Quirós, C.L., 2011. Guía para el cultivo de papaya en Costa Rica. MAG/UCR/INTA, San Jos
e, Costa Rica. Retrieved from http://www.mag.go.cr/bibliotecavirtual/F01-10190.pdf.

samples for a statistical treatment and taking into account the variability that may be encountered in the data. The interpretation of tissue analysis by the sufficiency range method is normally done by the direct comparison of leaf or petiole nutrient concentration of the sample against the previously defined standards. For C. papaya L., different thresholds of nutrient concentrations have been proposed (Table 42.2).

6.3 Complementary methods for diagnosis of nutrient constraints As the sufficiency range method has some limitations for the use and interpretation of tissue leaf analysis, it is necessary to evaluate alternative methods for the detection of nutrient constraints. The dilution effect method can be a useful concept for this purpose. The dilution effect concept ( Jarrel and Beverly, 1981) states that the concentration of the plant at some time is a function of the nutrient uptake and its production of dry matter. Therefore, older organs with higher amounts of dry matter have lower concentrations of nutrients, as a consequence of the nutrient dilution in the new tissue produced. Jarrel and Beverly (1981) summarized the dilution effect concept with examples of different factors such as light, water availability, temperature, and application of other nutrients, all affecting the dry matter production and consequently the concentration of nutrients in the plant. Jarrel and Beverly (1981) showed that, when one element is limiting the biomass production (i.e., phosphorus), there is an increase in the concentration of the other nutrients (i.e., nitrogen and potassium). When this limiting nutrient’s availability is improved by application of fertilizer, this leads to an increase of dry matter. However, for the other (nonlimiting) nutrients, there is less reason to take up more of them by the plant; hence, a decrease in the concentration of these other nutrients occurs; a slightly higher uptake is overcompensated by the dry matter increase. This complexity of interactions between nutrient concentrations in the plant makes it very difficult to infer a real sufficiency status for optimal yields, as the interpretation of plant tissue analysis is valid only for the

615

7 Nutrient management

TABLE 42.3 Increase in leaf nutrient concentration in a K-deficient solution treatment compared with a complete treatment. Leaf nutrient concentration (%) Treatment

K

N

P

Ca

Mg

Minus K

0.40

3.46

1.49

3.61

2.21

Complete

1.58

2.25

0.82

3.61

1.21

Data from Cibes, H. R., Gaztambide, S., 1978. Mineral-deficiency symptoms displayed by papaya plants grown under controlled conditions. J. Agr. U. Puerto Rico, 62(4), 413–423.

specific moment when the plant was sampled. Any measure that is taken, for example, fertilization for the limiting nutrient, changes the relative availability of all nutrients, and another nutrient may become growth limiting. Taking into account the temporal specificity of the plant analysis and interactions between nutrients in the plant, the dilution effect concept can be applied for the identification of nutrient deficiencies, assuming that an organ developed under nutrient scarcity cannot develop its maximum potential size or growth. Nutrient-deficient plants have limited growth and consequently higher nutrient concentrations of all the nutrients when compared with plants without a nutrient restriction for the growth (without deficiency). This behavior is found for all the nutrients except that one(s) that is causing the growth restriction. So, it is possible to have a more customized (varieties, regions, etc.) interpretation and avoid some of the problems found with the interpretation with the sufficiency range method. The interpretation of leaf analysis using the dilution effect concept can be exemplified using data of Cibes and Gaztambide (1978) for C. papaya cv. Solo (Table 42.3). In this example, it can be seen that, when a certain nutrient limits the growth of the crop (for this example is potassium), a larger concentration of all the other nutrients is observed because potassium limits the production of dry matter. If K is supplied in sufficient quantities, its concentration increases, and the concentrations of the other nutrients decrease (less additional uptake than additional dry matter production). From the comparison of these two treatments, it is immediately (and quantitatively) clear which element is limiting yield. For the success of the interpretation of nutrient deficiencies using the dilution effect concept, the need of choosing a right reference sample to compare (i.e., a high yield plant, without deficiency symptoms, etc.) is crucial. As the dilution effect concept is useful for crops in general, this need to choose the correct reference is also needed for other crops than papaya. As a final remark for the methods of chemical analysis of plant tissue, there are other methods for the interpretation that consider interactions between nutrients, for example, the Diagnosis and Recommendation Integrated System (DRIS). DRIS norms have been developed for several crops, and indeed, there are available for papaya (Bowen, 1992; Costa, 1995). However, due to the specificity of the norms to the conditions in which they were developed, their implementation for other regions or with other varieties requires calibration for specific conditions.

7 Nutrient management This section identifies the high nutrient requirements of papaya for high yielding systems and addresses yield gaps and common empirical approaches to determine fertilizer applications. Where papaya can be a high yield crop, this may be associated with significant leaching and contamination of groundwater. Also, this conflict is elaborated in this section.

7.1 Nutrient requirement for C. papaya L Reports about nutrient requirements for papaya are scarce. Cunha and Haag (1980), in Brazil, characterized the nutrient requirements for papaya. They used plants that were not previously submitted to any breeding program, and their potential yield is not comparable with modern hybrids that are used around the world. For their study, Cunha and Haag (1980) obtained a yield of 30,000 kg/ha. In Costa Rica, modern cultivars of C. papaya have been developed by a breeding program. As a result, the currently grown hybrids show a very high potential yield. For example, one grower mentioned to have achieved yields around 150,000 kg/ha in Costa Rica with the cultivar Pococí, and in

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42. Diagnosis and management of nutrient constraints in papaya

Brazil, (Campostrini et al., 2018) mentioned in a personal communication that papaya from the Formosa group can achieve the same (unofficial) yield. Official data for Costa Rica of the year 2014 show an (country specific) average yield around 100,000 kg/ha (FAO, 2018). Similar yields have been reported by Chan (2009) and by Gomes Oliveira and Correa Caldas (2004) in Brazil (93,000 kg/ha) and in Belize (113,000 kg/ha) (FAO, 2018). Despite the high potential yields of papaya, the average global yields in papaya for the year 2017 were around 29,540 kg/ha (FAO, 2018). This illustrates the huge yield gap in C. papaya production systems around the world and the high potential for the improvement of its production. The worldwide yield gap is in the order of 70%, considering a potential yield of 100,000 kg/ha or even higher as some yield estimates suggest. Some of the reports of low yields in papaya are related to diseases, principally viral diseases (Tennant et al., 2007), water management problems and water scarcity and related effects on the net photosynthesis of the crop (Campostrini and Glenn, 2007; Campostrini et al., 2018; Lima et al., 2016; Lopes Pec¸anha et al., 2017), and nutrient management. To obtain high yields, C. papaya L. requires a considerable amount of water and nutrients. Kumar et al. (2010) stated that the crop has a demand of 310, 105, 530, 332, and 185 kg/ha of N, P, K, Ca, and Mg, respectively. A similar study developed in Costa Rica, on volcanic ash-derived soil, showed a nutrient demand of 354, 44, 413, 124, 64, and 40 kg/ha of N, P, K, Ca, Mg, and S, respectively (Fallas et al., 2014). Comparison of these numbers reveals that they are different. There are several reasons that this may be the case. As already emphasized earlier in relation with the dilution effect, the relative availability of the different nutrients plays a role. This can be generalized toward other growing conditions, such as available light, temperature, and water availability. This underlines the need to characterize the important environmental conditions well enough, in publications such as these. The nutrient demand follows the development of dry matter of the crop. For a fast-growing crop like papaya, this results in a very high nutrient demand in short periods of time. Figs. 42.5 and 42.6 adapted from Fallas et al. (2014) show the total nutrient uptake and the nutrient demand per each stage of development. During the beginning of the growth period (from transplanting up to 2 months after transplanting), the nutrient requirements are lower than 4% of the total amount quantified at 8 months after transplantation (MAT). This behavior is in agreement with the low leaf area index of the plant at this stage and its consequently low capacity of light interception for the production of assimilates. The soil supply can play an important role during this period, but it is necessary to consider also that, during the first days of the establishment, the root system is small and its capacity to obtain the necessary nutrients may be a limiting factor. From the second month after transplantation and due to the fast growth rate of the crop, there is an increase in leaf area and light interception. Hence, the requirement for nutrients increases considerably, and fertilization and correction of nutrient constraints require even more attention from this period onward. The reason is that the quantities and proportions of nutrients required in this case may not be supplied by the soil system. Note that in Figs. 42.5 and 42.6, the sampling plant at the stage between 3 and 4 MAT was missing, so the quantity shown at 5 months corresponds to the uptake between 3 and 5 MAT. As observed in Figs. 42.5 and 42.6, in general, the stage of maximum nutrient requirement in papaya is related to the period just before the beginning of the harvest (i.e., about 7–8 months after transplantation). In this stage, the plant is producing new vegetative growth, and at the same time, it has a big requirement for the fruit filling because fruits are a strong sink for nutrients in the plant. At this phenological stage, the plant has fruits in all the different stages of development, and the production of vegetative and reproductive structures (flowers and fruits) is continuous. Papaya fruit development is characterized by a low accumulation of sugars from the anthesis up to approximately 100 days after anthesis; after 100 up to the harvest (approx 140 days after anthesis), the sucrose content in the fruit increases from less than 5 to approximately 40 g kg of fresh weight; fructose and glucose content increases more than two times from day 100 to day 140 after anthesis (Zhou and Paull, 2001). This production implies the high demand for nutrients when papaya is approaching its harvesting stage (period from seven to eight MAT in Figs. 42.5 and 42.6). It is important to emphasize that papaya has a hollow stem and does not accumulate starch; it is a plant with a low capacity to store assimilates. According to Jim
enez et al. (2014), the production of assimilates that is needed to accommodate the high fruit load requires a steady flow from the leaves. Figs. 42.5 and 42.6 give an impression of the required amount of nutrient for each stage, which has implications for fertilizer recommendations.

7.2 Management aspects related to soil acidity Papaya is native to tropical Central America. It is a crop that is preferably cultivated in high-fertility and welldrained soils. Nevertheless, it is adapted to soils with a broad range of chemical and physical properties. In Costa Rica, papaya is found under natural conditions in different regions, and it is also cultivated in soils that range from acid Ultisols in the region of Upala to more fertile Inceptisols with soil pH values near 7 and high Ca and Mg contents like the Central Pacific Region.

617

7 Nutrient management

N

250 Total

100% Increase

Vegetative

200 g per plant

150

60% 48%

100

40%

50

15%

20%

16% 9%

4%

0,0

0

8%

0% 0

1

2 3 4 5 6 Months after transplant (MAT)

7

8

0

1

2 3 4 5 6 Months after transplant (MAT)

7

8

P

30

100%

Total 25

Increase

Vegetative

Total

80%

Fruit

20 g per plant

Total

80%

Fruit

60%

15

44% 40%

10 5

20%

0

0%

10%

1

2 3 4 5 6 Months after transplant (MAT)

7

8

K

300

0

13%

17%

3%

0,0 0

13%

1

2 3 4 5 6 Months after transplant (MAT)

7

8

100%

Total 250

80%

Total

Fruit

200 g per plant

Increase

Vegetative

60% 150 40% 100

27% 20%

50

11%

13%

4%

0,0 0

13%

32%

0% 0

1

2

3

4

5

6

Months after transplant (MAT)

7

8

0

1

2 3 4 5 6 Months after transplant (MAT)

7

8

FIG. 42.5 Nitrogen, phosphorus, and potassium uptake by C. papaya L. cv. Pococí in the Atlantic Region of Costa Rica during a 8-month period. Modified from Fallas, R., Bertsch, F., Barrientos, M. 2014. Curvas de absorción de nutrientes en papaya (Carica papaya L.) CV. “Pococí” en las fases de crecimiento vegetativo, floración e inicio de cosecha. Agronomía Costarricense, 38(2), 43–54.

The soil pH does not appear to have a direct impact on papaya growth at the commonly found soil pH values. Marler (1998), studying the effect of the pH on dry matter production at the initial vegetative stage of papaya and varying the pH in the substrate solution from 3 to 9, did not find significant differences of dry matter production in the pH range from 4 to 9. Only when exposed to pH 3, the plants showed growth problems, and lower dry matter yields were accomplished. Although soil pH may not affect the dry matter production of papaya directly, its indirect effects can be more important. For example, soil pH can also play a role on papaya growth if it has a modifying effect on the solubility of nutrients in the soil. Therefore, pH is a necessary parameter to consider when aiming at a high nutrient use efficiency. The use of high quantities of fertilizer in intensive systems can also lead to the development of soil acidity problems if it causes a lowering in the soil pH values. Hence, fertilizer-pH interactions may be found in both directions. Additionally, low soil pH values commonly are correlated with low soil Ca and Mg contents, elements that are required in considerable

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42. Diagnosis and management of nutrient constraints in papaya

Ca

g per plant

90

1.0

80

Total

70

Vegetative

60

Fruit

Increase Total

0.8

50

0.6 45%

40 0.4

30 20

11%

10

4%

0.0 0

3%

0.0 0

1

2 3 4 5 6 Months after transplant (MAT)

7

8

0

1

2 3 4 5 6 Months after transplant (MAT)

7

8

Mg

45

1.0

Total

40

g per plant

21%

16%

0.2

Increase

35

Vegetative

30

Fruit

Total

0.8

25

0.6 43%

20 0.4

15

21% 10

0.2

11% 6%

5

9%

10%

0.0

0

0.0 0

1

2 3 4 5 6 Months after transplant (MAT)

7

8

0

1

6 2 3 4 5 Months after transplant (MAT)

7

8

FIG. 42.6

Calcium and magnesium uptake by C. papaya L. cv. Pococí in the Atlantic Region of Costa Rica during an 8-month period. Modified from Fallas, R., Bertsch, F., Barrientos, M. 2014. Curvas de absorción de nutrientes en papaya (Carica papaya L.) CV. “Pococí” en las fases de crecimiento vegetativo, floración e inicio de cosecha. Agronomía Costarricense, 38(2), 43–54.

quantities by the crop (Fig. 42.6). Consequently, the need for liming low pH soils for papaya production is also related to the supply of Ca and Mg. Regarding soil acidity, the high aluminum content in soil is a big concern for papaya producers. This cation has been related to the inhibition of root growth in several crops (Ryan et al., 1992), and it could have a notorious impact on the overall nutrient uptake and efficiency (Zhao and Shen, 2018). However, the effects of aluminum on papaya are not well established, for example, there is only one report about high Al concentrations that cause toxicity for papaya. (De la Fuente et al., 1997) found that, in nontransgenic plants, the root growth is inhibited at Al concentrations of 50 μM, while, on transgenic plants altered for the citrate synthesis, the roots grew even at concentrations up to 300 μM. Besides Al toxicity, also toxicity, for example, of Mn and Fe, may have to be taken into consideration, but information about Fe and Mn toxicity for papaya is not available. When lime is applied to supply Ca and Mg for the crop, the commonly used methods to calculate the lime requirement (Cochrane et al., 1980; Quaggio et al., 1985; Shoemaker et al., 1961) are not very useful as they cannot predict the availability of those nutrients for the crop in the different stages, so alternative and preferably more mechanistic methods are necessary to improve the calculations of lime requirements for the crop; these tools can also help to improve the timing of application for papaya, as nowadays in some cases, lime is applied on acid soils only at the beginning of the crop cycle. Examples of alternative methods for liming calculation are discussed in the section of the mechanistic modeling approach.

7.3 Fertilizer dosage for papaya using an empirical approach As explained earlier, papaya is a very high nutrient demanding crop, so it is predictable that the yield increased when the rates of fertilizer for the crop are increased. Several experiments have been conducted to evaluate the effect of nitrogen, phosphorus, and potassium on yield response, and recommendations of fertilizer rates for different regions have been based on the results of those experiments.

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Kumar et al. (2010) recommended a fertilization with 300, 300, and 300 g of N, P2O5, and K2O, respectively, per plant per year, based on an evaluation of fertilizer rates under different field locations. With a similar kind of experiment, Gomes Oliveira and Correa Caldas (2004) recommended a yearly fertilization dose of 347 and 360 kg/ha of N and K2O, respectively, for the region of Cruz das Almas-BA, Brazil. In Costa Rica, Fallas developed a fertilization experiment with N, P, and K rates based on the results of Fallas et al. (2014). For this experiment (unpublished), nitrogen and potassium rates were as high as 700 kg N/ha and 700 kg K2O/ha, respectively, distributed in a period of 9 months and using a plant density of 1600 plants per hectare. The results of this fertilization experiment showed the high response of papaya to high rates of fertilization (up to 132 tonnes of fresh fruit per hectare in a dystric soil) and also a strong interaction between the nutrients on the yield of the crop (Fig. 42.7). The recommendation of fertilization based on regional experiments of nutrient rates albeit practical and in some cases functional most of the time ignores the processes that govern the real availability, uptake, and movement of the nutrients in the soil-plant-atmosphere system, so there is an implicit risk to contaminate groundwater sources. Nevertheless, this kind of experiments in combination with methods that consider the relationships between nutrients and its relationship with environmental factors is a good option to detect limiting nutrients for specific conditions, for the correction of the nutrient constraints, and for the improvement of fertilization programs. It is well known that different growth factors (light, temperature, water, and various nutrients) cannot be considered independently of fertilization, even though, for regions, their spatial variation may be small (e.g., light and temperature). The main reason is that different growing factors may affect the response of each other by the crop. Specifically for nutrients, interactions for uptake and crop growth have been recognized by Janssen et al. (1990). They developed and parameterized the model QUEFTS that accounts for such interactions, which for papaya are very necessary as shown in the Fig. 42.7. The basic concept is related with Liebig’s law of the minimum, according to which the nutrient that is lowest in supply determines the crop’s yield and uptake of other better available nutrients. As the limiting nutrient is applied in the form of fertilizer, its uptake but also that of other nutrients increases. The ratios between uptake rates of different nutrients change in dependency of which nutrient is limiting and which rates can be accomplished, for example, very deficient or slightly deficient availability of the limiting factor ( Janssen et al., 1990; Sattari et al., 2014). Accordingly, fertilization with the limiting nutrient may lead to synergies if at the same time also other nutrients are fertilized. This multidimensionality of critical nutrients and other growth factors (incoming radiation and water availability) complicates modeling of optimal conditions and sets high demands on the experimental basis of fertilization (taking cross factor effects into account).

FIG. 42.7 Number of fruits per plant at the stage of the first harvest in Carica papaya L. cv. Pococí as a function of rates of nitrogen and potassium in field conditions, in the Atlantic Region of Costa Rica. Applied fertilizer rates correspond to the cumulative amount of a 9 months period.

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42. Diagnosis and management of nutrient constraints in papaya

As Sattari et al. (2014) conclude, other factors than N, P, and K (considered nowadays in QUEFTS) may be important and vary considerably from region to region. Major limitations of the QUEFTS approach are also the disregard of soil heterogeneity, weather variability, and the profound experimental effort to parameterize the model for a certain region and crop. Nevertheless, this latter effort is feasible if a region regarding those other factors (climate, hydrology, and soil type) is sufficiently homogeneous and of sufficient size to make extensive plot fertility experiments worthwhile. Then, this approach is no doubt far more appropriate than assessments that disregard interactions between growth factors. On the other hand, there are some remarks to consider before the implementation of the QUEFTS approach, for example, in the Atlantic region of Costa Rica with volcanic soils, the model predictions for corn and grass differed substantially from the real data (Nieuwenhuyse, 1988), which was related to the behavior of allophanic soils regarding N mineralization and P retention. Also for papaya, as far as the authors are aware, no data are available about the simultaneous uptake of nutrients and its yield under variation in the supply of the major nutrients. These data are necessary to establish the potential supply and the actual uptake of N, P, and K as defined by Janssen et al. (1990). At the moment, however, in Costa Rica and other countries, the interaction between different nutrients is not always taken into consideration. Then, instead, for each nutrient, the fertilizer requirement is based on known crop needs, selective extractions of soil (and not calibrated for papaya), and estimated fertilizer efficiency. For this approach, the calculation of nutrient requirement is based on the data of the uptake of nutrients by the crop, for example, according to information published by Cunha and Haag (1980) and Fallas et al. (2014). The calculation of the available quantity of nutrient is based on an empirical calibration of nutrient concentrations determined by a soil solution extractant, such as Mehlich I, II, III, KCl, and Olsen. This calibration is based as a response of the crop to fertilizer additions under different soil nutrient concentrations determined by the extractant. This kind of calibration requires a big quantity of experiments in different soil conditions. Therefore, the information of a specific calibration for papaya does not exist as the authors are aware. If the solution concentrations in the soil are over the defined critical concentration, it is assumed that the soil can supply nutrients for the crop. If this is not the case, it is assumed that is necessary to apply all the plant requirement by means of fertilizer additions (organic and/or inorganic). Finally, the fertilization efficiency could be estimated according to the environmental conditions (like precipitation and soil moisture) and the geochemical characteristics of the soil, which are principally related to the current content of the nutrient and its reactivity with the soil matrix. Most of the times, this calculation of the efficiency is a subjective decision, and it is assumed big losses to avoid the limitation of the crop growth and yield. This final aspect is reprehensible from an environmental perspective, and it could also lead to contamination of groundwater sources and higher emissions of greenhouse gases. Having emphasized the procedure of this empirical approach and its limitations, the amount of required fertilizer for each stage of the crop can be calculated with

Requirement of the crop kg nutrient=ha  Soil supply kg nutrient=ha  100 (42.1) kg nutrient=ha ¼ %Efficiency of fertilizer Regarding fertilization management by means of organic fertilizers and biofertilizers, incidental investigations only are available. They can be useful in practice and can give an idea about the possible outcomes of using organic materials and microorganisms, but most of these studies lack explanations about the mechanisms that govern nutrient availability and uptake by papaya under different conditions of the soil-microorganisms-plant-atmosphere continuum. Consequently, it is difficult to establish general recommendations of organic amendments and microorganisms for the diverse and heterogeneous conditions in which papaya is grown around the world. Despite these issues, the beneficial effects of organic matter and beneficial microorganisms on the fertility, productivity, and sustainability of the soils are of great importance. About biofertilizer use in different conditions of climate, P availability, etc., Sch€ utz et al. (2018) conducted a metaanalysis and gave some insight into the use of these products in several conditions. Because of the limited support of observations in the literature, a generalization as is the aim in this chapter is still not in reach.

7.4 A mechanistic modeling approach to management In most countries, even nowadays, soil fertilization and fertility recommendations are strongly based on empirical approaches as were mentioned in the previous sections. However, models are finding an increasingly large position in

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soil-plant fertility research, and some approaches are shortly discussed here. One of these applications of modeling in papaya could be to improve liming practices for the crop. As already described, papaya requires big quantities of calcium and magnesium during the stages that do not necessarily coincide with the time of lime application. Chemical modeling can improve the assessment of the requirement of lime, regarding time and quantity, for example, in case of slow acid-base reactions. This may help avoid the need (e.g., Cochrane et al., 1980) to multiply the calculated quantity of lime per hectare by a factor of 2 to account for nondetermined Al sources. Considering the limitations of the empirical methods and its slow development during the last decades, an alternative (but knowledge demanding) method to improve Ca and Mg application practices for papaya is the use of multisurface adsorption models, which are based on mechanistic descriptions of element adsorption reactions. Examples of multisurface models are implemented by the codes ORCHESTRA (Meeussen, 2003), MINTEQA2 (Allison et al., 1991), ECOSAT (Keizer and Van Riemsdijk, 2009), and PHREEQC (Parkhurst and Appelo, 1999). Multisurface adsorption models are capable to consider multicomponent interactions within reactants and with several adsorption surfaces such as organic matter, clay silicate, and crystallized and amorphous iron hydroxides (Peng et al., 2018; Weng et al., 2001). This kind of models considers the global adsorption surfaces as the addition of each individual soil component and calculates the equilibrium in the system. The authors are aware that this kind of models have not been used for liming dosage calculations, but such models have been used to explain the effect of liming on C dynamics in the vadose zone using the HP-1 model (Thaysen et al., 2014). Also, predictions have been made of pH and Al, Ca, Mg, Zn, Cu, Cd, and Ni activities of a soil exposed to acidification treatments (Fest et al., 2005). Therefore, multisurface models can be useful to improve recommendations for the lime requirement for crops as papaya grown in acid soils, to estimate Al and Fe activities in the soil solution, as well as Ca and Mg availability, which are all the principal targets of liming practices in plant production systems. Another multicomponent model that might be useful for the improvement of liming management is UNSATCHEM-2D (Šimůnek and Suarez, 1994); this model considers major ion chemistry and calcite and dolomite solubility and precipitation. An alternative approach for nutrient management, which commonly is much more demanding in terms of computer CPU demands, is the extension of agrohydrological modeling toward nutrients. Agrohydrological models are nowadays strongly focused on understanding water availability to crops and used to schedule irrigation requirements, to anticipate drought yield reductions, and to appreciate salinity stress by crops (Kuhlmann et al., 2012; Noory et al., 2011). This approach, for water availability, usually solves the unsaturated water flow equation (the well-known Richards equation) for a one-dimensional (vertical) water column. For the water flow, use can be made of several computer codes, such as SWAP (Kroes et al., 2017) and HYDRUS-1D (Šimůnek et al., 2008). The 3-D version of the Richards equation that combines Darcy’s law with the continuity equation is ∂θ ¼ r  ðKrH Þ  S ∂t

(42.2)

where θ is the volumetric water fraction, t is time, K is the unsaturated soil hydraulic conductivity tensor, H is hydraulic head, and S the sink term for root water uptake. To account for RWU, evapotranspiration, and atmospheric forcing, the water flow models have to be equipped with plant root functionality, which is available in, for example, SWAP and HYDRUS-1D. Few models are available where the soil water model is interacting with a crop growth model (Vereecken et al., 2016), but they are available. A well-known example is the SWAP-WOFOST (Kroes and Supit, 2011) and HYDRUS-WOFOST combination (Zhou et al., 2012). Multidimensional models are also available, such as FUSSIM-3D (Heinen, 2014), PARFLOW (Kollet and Maxwell, 2008), and HYDRUS-3D (Šimůnek et al., 2016). Of great interest is also the advanced 3-D modeling by Vereecken et al. (2016). Examples where the next step, of combining nutrient transport with multi-D flow and RWU modeling, is OpenSimRoot model (Postma et al., 2017); also, pseudo multi-D flow and transport modeling have been undertaken. The rapid development of software and CPU power is indicative of the advances that can be expected in the next decade. Whereas modeling for soil systems as done by Vereecken et al. (2016) may quickly be extended toward the fate of fertilizer derived nutrients, it is worthwhile to point out some major gaps in our understanding. These gaps concern both water flow and chemical fate (in this chapter, we will focus on water-dissolved chemicals, solutes, rather than gas phase (e.g., N2O) or pure liquid (oil) compounds as studied by Rappoldt (1992) and Van Dijke and Van der Zee (1998), respectively). Water uptake: It is evident that, if the roots take up soil water for the transpiration stream inside the plant, this uptake is easier if more roots are present: more roots in a soil volume imply a shorter distance that water needs to travel toward the roots, hence, less flow resistance. For this reason, it is obvious that the so-called root density is

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42. Diagnosis and management of nutrient constraints in papaya

an important factor with regard to the uptake of water. The same is the case for the water content in soil: the more water, the easier it is for the plant uptake. The complexity arises if soil is heterogeneous with regard to the soil hydraulic functions (the hydraulic conductivity and the water retention function), and the water content varies in the root zone. In both cases, the plant needs to decide where to take the water. In our software, we need to program this decision: will the plant choose to use water from the place with highest water content, smallest water suction, or some combination of these? (Couvreur et al., 2014). The physical problem is that not only the local presence of water and its energetic status but also the supply rate of water by the soil to the root surface is important for RWU, which is controlled by the hydraulic conductivity function. In their study of RWU in a heterogeneous soil, Kuhlmann et al. (2012) noticed that a wrong choice in these issues could result in a reasonably wet soil with extremely desiccated parts, where the root zone had extracted all of its water: an unrealistic situation. The complexity of soil-plant RWU relationships is that not only soil controls this interaction, if it is in control to begin with. For instance, we are just beginning to understand plant physiological responses to water stress. Davies and Zhang (1991) gave evidence about plant hormones and root shoot signaling in this context. This suggests an active response of plants to stress that is completely ignored at the soil side of plant–soil research, to our best knowledge. Whereas it is commonly recognized that different plant species have a different susceptibility to drought and indeed to the impact of soil salinity, the different responses of one-species’ different genotypes have been recognized for both drought and salinity. Yet, it is not accounted for in any soil water model. These observations, which are (for genotypic differences) sometimes based on incidental evidence, are indicative that there is too little interaction between plant physiology and soil physics. Whereas we therefore are confronted with essential gaps in our understanding of water uptake by the plant root system, this is even more the case for nutrient uptake. Partly, this is inherent to the very different chemical properties of the nutrient components. Each component can be present in the soil system in the form of a range of species. An example is nitrogen (as NO3  , NH4 + , NO2, organic N, and so on), for which the different species show different sorption behavior and redox conditions under which they are present. The source of most nutrients is applied fertilizer, immisions from atmosphere or groundwater into the unsaturated soil and its root zone (RZ), and mineralization in situ. Just as with water, nutrient availability depends on the local quantity present in soil and the transport rate toward the root surfaces (or, adversely, away from these surfaces or out of soil). The multi-D version of the solute transport equation (the convection dispersion equation or CDE) combines the flux equation with the continuity equation and is given by ∂θC ∂f ðCÞ + ¼ r  ðθDrC  qCÞ  Sd ∂t ∂t

(42.3)

where C is the solute concentration in the water phase, f(C) is a function that describes solute present in a solid form (i.e., matrix), D is the hydrodynamic dispersion tensor, q is the Darcy water flow rate, and Sd represents sinks related with root uptake, volatilization, and degradation/transformation. Focused on nutrient availability for papaya, several issues of bioavailability in relation to the CDE are apparent, and these are now briefly introduced. 1. For most nutrients, chemical reactions occur both in the aqueous solution and between the solution and the soil matrix. These reactions may be adsorption/desorption and precipitation/dissolution, as well as complexation, the latter in solution. These reactions are typically dependent on the concentrations or activities of more than one solute; hence, for each of these so-called species, a version of the CDE can be developed. In fact, it is also possible to combine those CDEs for one CDE for the component, where the component is dependent on all species of, for example, nitrogen, or zinc, or another element. Models are available to deal with this “multicomponent” approach of solute transport, such as the well-known HP models (HYDRUS-PHREEQC combinations) ( Jacques et al., 2018) and ORCHESTRA (Meeussen, 2003). 2. Root nutrient uptake (RNU) is, just as root water uptake (RWU), dependent on the availability at different places in RZ and the transport of nutrients in the root zone (the replenishment). Accordingly, RNU depends on where nutrients enter the root zone and how often this happens (comprehensively called the “fertilization” of RZ) and the retardation due to chemical reactions. In the case of, for example, nitrogen, also, the other factors comprising Sd than root uptake are important. In modeling with the CDE, assumptions are required in the form of “boundary conditions” on how the concentrations are at the root zone and at the plane “halfway” between individual roots. But because the root architecture is spatiotemporally very variable, these boundary conditions change a lot during a growing season. Modeling can hardly (at this time) keep track of these changes, and therefore, it is necessary to find reasonable approximations from which we can still learn on the practicalities of how to fertilize papaya. An example

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is the axially symmetric approach of the CDE by Roose et al. (2001) for the transport rate of a nutrient to the single plant root. They assumed that the uptake kinetics at the root surface is given by Michaelis-Menten (MM) kinetics. Whereas MM kinetics are flexible, the switch between limiting cases of MM, such as zero sink, linear concentration dependency of uptake rate, and constant uptake rate, is generally not well known. Moreover, it is likely dependent on water transpiration rate and other complications (see Hinsinger et al., 2011). 3. It has been well recognized that the root architecture is strongly spatiotemporally dependent. Indeed, models have been developed for important crops, to describe such dependencies throughout the growing season. For a crop as papaya, such a model has not yet been parameterized as far as the authors are aware. 4. Nutrient applications nowadays occur regularly for papaya, because their availability needs to be high at all times, to ascertain top yields, and because the soils with commonly not only a high hydraulic permeability but also a large rainfall excess are prone to have much drainage water and therefore much leaching of nutrients. For fertilization efficiency and sustainability of papaya management, it is important that the nutrients are taken up by the crop and not leached into groundwater. To attain this goal, a careful balance is needed for application and atmospheric (rainfall) forcing. Despite the complexity that was pictured so far, the application of “distributed modeling” is promising. The reasons for that are diverse. For instance, Rappoldt (1992) already showed that geometric complexity (in his case of aggregates, in our case of the root system) can often be captured well in simplified equivalent behavior. It is plausible that this is also the case for RWU and RNU, as Couvreur et al. (2014) showed. They simplified the complex root system to elementary building blocks and showed that it is possible to separate the impacts of (i) water content and (ii) root architecture with appropriate cases that are simulated or perhaps measured (see Vereecken et al., 2016). If field data are available, the soil hydraulic parameters and the root water uptake parameters can also be estimated by inverse modeling using the Couvreur’s approach (Cai et al., 2017). The necessary data to accomplish this kind of modeling for papaya include time series with data of soil water contents, its potential, and root distribution at different spatial scales. We have to admit that, at this moment, the complexity of the root zone water and nutrient dynamics under temporally variable fertilization and rainfall regimes can only be modeled in broad features. Still, such modeling may result in important benefits. For instance, the recognition that most of the nutrients show relatively simple behavior can be approximated by (i) no to little adsorption and high mobility (nitrogen N), (ii) linear adsorption with distinct smaller mobility (Zn, P, and B), and (iii) some intermediary cases (Ca and Mg). These stereotypes can be confronted with dynamic aspects such as irregular (but high rate) rainfall and irrigation, which are likely to leach the more mobile nutrients. For a sustainability analysis, these issues of availability to crop and hazard of leaching need to be combined. With modeling, this is possible if RWU and RNU in dependency of quantities in the root zone are sufficiently known. A combination of such issues is a challenge, but not without precedence. For instance, for water, the impact of highly variable inputs has been investigated by Rodríguez-Iturbe and Porporato (2004), for salt accumulation by Suweis et al. (2010) and Shah et al. (2011), and for sodicity by Van Der Zee et al. (2014). A translation of such “ecohydrological” model approaches toward adsorbing nutrients, as considered by Boekhold and Van der Zee (1991) for metals, seems perfectly feasible. In Fig. 42.8, the accumulation of “a nutrient” in soil is simulated with a model similar as used by Van Der Zee et al. (2014), where the application regime is varied. In one case, fertilizer is applied once a year, and in the other case, the same annual quantity is applied in 12 equal monthly applications. Furthermore, a season with leaching (i.e., rainfall and irrigation excess) is followed by one without leaching. We see that the fluctuation of the increasing concentration in the root zone is much larger for the annual application. This is logical, and in practice, it depends on many factors (amount of rainfall/irrigation, nutrient adsorption by soil, degradation, and uptake by plant) how large this fluctuation will be. Implicit to Fig. 42.8, also, the leaching will show a sawtooth pattern. This pattern will depend on whether the crop growth (and annual applications of fertilizer) occurs in the dry or in the wet season. Whereas this figure is only intended as an illustration, relatively simple models as used to prepare this figure can help in assessing trade-offs and optimize growing conditions and fertilization strategies. The development of such models for nutrient-crop combinations requires their parameterization with experimental pot, greenhouse, or field fertilization research, but the rationale can be illustrated with very basic considerations. For instance, consider a well-developed papaya stand, in a high yield cultivation scheme. To accomplish those yields, fertility research may identify which application rates and frequency are needed. The risk of leaching of nutrients depends on the soil hydraulic functions (i.e., the hydraulic conductivity and the retention curves). These functions reflect how much water will drain depending on the irrigation management. In combination with an uptake rate, drainage rates and nutrient concentration levels can be translated into nutrient leaching rates. Accordingly, fertilization and rainfall/irrigation rates can be translated into nutrient leaching rates. With a model, options for good

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FIG. 42.8 Simulation of soil nutrient accumulation (concentration) in the root zone (A) and the cumulative mass leached (B) as a function of annual or monthly application regimes.

2 1.8 1.6

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1.4 1.2 1 0.8 0.6 0.4 Yearly application Monthly application

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12

14

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18

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fertilization and also little leaching can then be identified. In stating this, we recognize that other aspects play a role: For instance, a 24/7 irrigation/fertilization scheme of every 30 min is unfeasible, but every 12 h might be feasible and every 7 days even more.

8 Conclusions and perspectives With mechanistic modeling, experiments can be interpreted and designed. Such experiments, to characterize the soil or its reaction to interventions such as fertilization, are crucial to provide the ground truth for the modeling. The models, though, combine the knowledge we have about the soil-plant system and, if well-parameterized, enable the search for optimal conditions in a cost-effective way: fast and with less demands on time and labor. A necessary step from experimentation toward interpretation is to recognize the interrelationships that have been formalized with the QUEFTS approach. So far, it has only been considered for nutrients. To account for water availability, a necessary step if this availability is highly variable is to use either “bucket” type of ecohydrological modeling

References

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or really distributed models. Despite that the later models are also confronted with several crucial gaps in knowledge, it is irrefutably true that, for water availability and irrigation management investigations, such models are already used. In fact, drought- or salinity-induced yield reductions are evaluated with such models and sometimes considered in liability or compensation measures. Based on the use of water modeling for crop yield improvement, it is clear that this can also be done for nutrient availability, despite shortcomings in our knowledge. Just as with experiments in the field, modeling can give us understanding of interdependencies between management, different crop growing factors, and yields. Future advances in modeling of water and nutrient uptake for papaya require the parameterization, calibration, and evaluation of solute transport models for soils in which papaya is grown. These solute transport models are necessary to account for nutrient availability and for the leaching potential of the applied fertilizer. The use of multicomponent solute transport models is interesting to account for the interactions between nutrients and their reactions in the soil, as for papaya, commonly, a mixture of elements in each fertilization is being applied. Parameterization and calibration of root water and nutrient uptake models are still pending. For the most common nutrient uptake models, basic parameters that are lacking are the concentration threshold that divides the nutrient uptake between the passive or the active uptake mechanism and parameters that describe the kinetics of the active nutrient uptake (Michaelis-Menten parameters).

Acknowledgments We gratefully acknowledge support and funding of this work by Ministry of Science and Technology of Costa Rica (MICITT), University of Costa Rica (UCR), and “Fundación FITTACORI.” We also appreciate that Pavan Cornelissen (Wageningen University & Research) made Fig. 42.8 and the permission of Antonio Bogantes (MAG-INTA, Costa Rica) and Eric Mora-Newcomer (University of Costa Rica) for using their figures and tables. This work was partially funded by the Dutch Science Foundation (NWO) project RUST under NWO-contract number ALWGK.2016.16.

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