I. Introduction

The nutritional diagnosis of crops is done with the results of chemical fertility analysis of soils, using the nutritional requirements of the studied species (Chacón-Pardo et al., 2013a; González, 2017; Herrera, 2015) and frequently the interpretation of nutrient concentrations in leaf tissue with widely used methods such as Critical Levels and Sufficiency Ranges (Bangroo et al., 2010; Savita et al., 2016). These methods are based on the individual comparison of the nutrient concentration in the sample with an accepted value of normality for a certain phenological stage without taking into account the relationships between nutrients and their possible imbalances (Kania & Callejas, 2011).

Because of the limitations caused by traditional interpretation methods, the Diagnosis and Recommendation Integrated System (DRIS) was proposed, which is a set of norms used to evaluate the nutritional status of a crop, using the composition of plant tissues, the chemical fertility of the soil, the climatic conditions, and the integrated management practices of such crop as the reference (González, 2017; López-Montoya et al., 2018).

DRIS, which is the acronym for "Diagnosis and Recommendation Integrated System", was proposed by Beaufils (1973), based on studies on physiology and plant nutrition, initially in rubber trees cultivated in Vietnam and then in maize and sugarcane in South Africa. In 1975, Sumner introduced DRIS to the United States (González, 2017). Since then, diagnosis norms have been determined for several extensive, forage, fruit, and vegetable crops and for timber species (Bangroo et al., 2010; Kania & Callejas, 2011; Savita et al., 2016).

The dynamic processes of leaf composition require a holistic diagnostic evaluation beyond the comparison of analytical results of each nutrient, comparison to the critical value or existing sufficiency ranges for each particular species (Kania & Callejas, 2011). DRIS uses the results of leaf analysis or chemical fertility of the soil to identify the most limiting nutrient in the development of a crop. The evaluation is done by comparing the relative balance of the content of a nutrient against the DRIS nutritional diagnosis norms established for the species (Savita et al., 2016). According to Walworth and Sumner (1987), the theoretical bases of this system are founded on the fact that the relationship between nutrients is a better indicator of the nutritional status of the plant than the isolated values.

Maximum productions are achieved when the relationships between these nutrients are close to an optimal value obtained from the selection of a population with high yields (reference population). DRIS indices can be calculated for the identification of deficiencies, excesses, or ideal values of nutrients in a crop. Furthermore, DRIS indices establish the Nutritional Balance Index that represents the global balance of the nutrients involved in the nutrition of the plant (Bangroo et al., 2010; Chacón et al., 2013b).

In the last ten years, three scientific reviews have been published by Bangroo et al. (2010), Kania & Callejas (2011), and Savita et al. (2016) on the development of methodological proposals to obtain DRIS norms in various crops of agronomic interest and the calculation of their respective nutritional indices, but they do not elucidate the real functionalities that this diagnosis method in agriculture. This review aimed to present the applications and uses of the Diagnosis and Recommendation Integrated System developed during the last 10 years in agriculture worldwide through the systematic review of databases such as Redalyc, Scielo, ScienceDirect, Scopus, Springer Nature, Taylor & Francis, and Web of Science.

II. Establishment of DRIS norms

To make use of this method for nutritional diagnosis in a crop, it is necessary to establish DRIS norms or standards that can be obtained for the plant tissue and/or physical and chemical properties of the soil. The best norms are generated from a large database for a specific genotype within a particular agro-ecological zone, where variables of relief, climate, integrated crop management practices, crop yield, concentration of nutrients in the plant, and chemical and physical variables of the soil are related to the soil-plant-environment relationship (Behera et al., 2016; Chacón et al., 2013a; Franco-Hermida et al., 2017, 2020; González, 2017; Herrera, 2015; Landriscini et al., 1997; Pereira & Justino, 2021).

Local calibration of DRIS norms offers greater precision in the diagnosis of deficiencies or imbalances in a crop, using those produced for other regions and providing reliability, rigor, and accuracy (Franco-Hermida et al., 2017, 2020; Landriscini & Galantini, 2018).

In the absence of local norms, those developed under other edaphoclimatic conditions can be used as long as the nutrient concentrations in the high-yielding population used to generate the norms are similar to the nutrient concentrations of the high-yielding population under study (reference population) (Kania & Callejas, 2011). That is, local regulations must have strong statistical support to be applied in environments other than where they were generated (Landriscini & Galantini, 2016, 2018).

III. Calculation of DRIS indices

Once the DRIS norms for a crop are known, along with reference average values of the concentrations of the nutrient pairs in the high-yielding population (Kania & Callejas, 2011), the DRIS indices (DRIS-I) are found to identify possible nutritional imbalances and to define the order of nutrient requirements at the foliar or edaphic level (Franco-Hermida et al., 2017).

The indices are established when determining the calculation of the functions, which in turn, consider the relationships between nutrients analyzed in the sample (tissue and/or soil) and the functions of the DRIS indices (Herrera, 2015; López-Montoya et al., 2018). After the calculation of the function of each relationship, the DRIS-IN of the nutrients in the study are obtained (Kania & Callejas, 2011; Giner, 2015).

Subsequently, the Nutritional Balance Index (NBI) is calculated, which is an index of production related to crop yield, which determines the general balance of nutrients involved in plant nutrition (Chacón-Pardo et al., 2013a). Likewise, the Nutritional Balance Index (mNBI) is calculated, which qualifies the degree of sufficiency of each nutrient (Herrera, 2015; López-Montoya et al., 2018).

IV. Application and uses of DRIS in agriculture

The DRIS system has had a dynamic advance worldwide during the last 10 years, which has made it possible to address plant nutritional diagnosis holistically. This system is becoming a key tool for the integrated management of production systems, expanding its use in agriculture. The studies below suggest the importance of these studies being done at the local and/ or regional level on a specific genetic material or cultivar to achieve reliable results capable of validation (Table I).

TABLE I

Application and use of DRIS systems in agriculture worldwide during the last 10 years.

*GIS: Geographic Information System. Source: Own elaboration.

According to the consolidated results of 63 studies (Table I), eight uses for DRIS with a wide application in agriculture were elucidated, along with five important less-impactful uses given their current apex, detailed as follows:

A. Nutrient balance in leaf tissue

After the establishment of DRIS norms, DRIS-I, NBI, and mNBI indices are applied to identify deficiencies, excesses, or nutritional balance of macronutrients (N, P, K, Ca, Mg, S) and micronutrients (Fe, Mn, Zn, Cu, Mo, B) in leaf tissue. The results of these studies have been applied mainly in Brazil, in crops of wide commercial interest such as cotton, banana, soybean, potato, cacao, and sugarcane (Araújo et al., 2019; Dos Santos et al., 2019; Oliveira et al., 2019; Pereira et al., 2013; Pereira & Justino, 2021; Rodrigues-Filho et al., 2021), and in other agricultural species, including beans, garlic, carrots, atemoya, and cactus pear (Table I).

In Colombia, DRIS studies have been carried out on rubber trees, banana, and roses (Chacón-Pardo et al., 2013a; Franco-Hermida et al., 2017; González, 2017), along with studies in Africa on maize (Dagbenonbakin et al., 2013), in India on banana (Raghupathi & Srinivas, 2014), and in Argentina on wheat (Landriscini & Galantini, 2018).

B. Dynamics of mineral elements according to the phenological stage

The accumulation and absorption of nutrients is a continuous process during the growth of plants, mediated by metabolism, phenological stage, and response to environmental factors. The DRIS method elucidates the dynamics of macro and micronutrients in plants for a determined phenological period. Also, it identifies the order of requirement, excess, deficiency, and nutritional balance. The fertilizer application rate, depending on the nutrient demand for each of the growth stages, can maximize yield and increase the efficient use of fertilizers.

DRIS usefulness has been evidenced in studies on timber species, such as Peroba amarela in the nursery phase in Brazil (França et al., 2020), in the development for Red Cedar in Mexico (Parra et al., 2020), and in adult teak trees in Indonesia (Chanan et al., 2019).

In perennial and short-cycle fruit trees in Brazil, research in the nursery, development, flowering, and fruit filling phases has been documented for crops such as coconut, oil palm, Conilon coffee, and carrot (Bastos et al., 2017; Medeiros et al., 2015; Partelli et al., 2018; Reis et al., 2017), along with studies in India on pomegranate (Gosavi et al., 2017), on apple in China (Shun-feng et al., 2018), on pineapple in Colombia (López-Montoya et al., 2018), and on apple, peach, persimmon, fig, vine, and mandarin in Japan (Freire et al., 2019). Research has also been found for roots and tubers, including yam in Africa (Dagbenonbakin et al., 2012) and in flowers, mainly roses, in greenhouses in Colombia (Franco-Hermida et al., 2013).

C. Critical Levels and Sufficiency Ranges

DRIS proposes five groups for the Sufficiency Ranges in leaf tissue and/ or soil (deficiency, tendency to deficiency, sufficient, tendency to excess, and excess), and an optimal level for Critical Concentration. Both the Sufficiency Ranges and the Critical Concentration are a reliable tool for nutrition diagnosis, enabling the development of programs with economic feasibility and environmental sustainability. DRIS indices establish the probability of response to the application of nutrients (Herrera, 2015).

In recent years, this method has been used to update and validate the Critical Levels and Sufficiency Ranges of soils planted with oil palm in Colombia (Herrera, 2015) and beans in Brazil (Partelli et al., 2019). The greatest use is at the level of leaf tissue for oil palm in Colombia (Herrera, 2015), carrot (Reis et al., 2017), orange var Pear (Machado et al., 2013), irrigated beans (Partelli et al., 2014), guava (Souza et al., 2015), and sugarcane (Da Silva et al., 2021) in Brazil, and aonla (Nayak et al., 2011), oil palm (Behera et al., 2016), and mango in India (Naik & Bhatt, 2017).

D. Soil-plant-environment relationship

Once DRIS norms have been defined for both soil and plant tissue, DRIS indices are determined to correlate the balance of nutrients in leaf tissue with the mineral elements present in the soil and their interaction with the relief and climatic factors, such as light, temperature, relative humidity, and rainfall, among others, to explain the nutritional response of plants from a holistic perspective.

Foliar analyses determine the more limiting nutrients for high yield, and soil analyses enable the correction of formulations in fertigation or edaphic applications. Said analyses are a powerful tool for integrated nutritional diagnosis (Gonzáles, 2017; Franco-Hermida et al., 2017). DRIS has been used successfully in Colombia for rubber trees (Chacón-Pardo et al., 2013a, Chacón-Pardo et al., 2013b), and roses in greenhouses (Franco-Hermida et al., 2017), cacao in Brazil (Oliveira et al., 2019), and banana in India (Raghupathi & Srinivas, 2014).

E. Nutritional balance from the use of fertilizers and green manure

The DRIS system uses the nutritional balance index (NBI) to elucidate the concentration and dynamics of nutrients in plant tissue thanks to the contribution of mineral elements through fertilization or the availability of these in the soil, as a result of the association of different crops with species of green manure. Studies on the use of nutritive solutions (chemical and organic synthesis) for hydroponic melons in Mexico (Gómez, 2018) and green manure in orange var. Pera in Brazil (Alves et al., 2014) and the nutrients available through the use of ash on sugar maple in Canada are documented (Arseneau et al., 2021).

F. Spatial variability and DRIS

Geographic Information Systems (GIS) analyze the spatial variability of the physical and chemical properties of the soil (Herrera, 2015) and the dynamics of biotic factors such as incidence of diseases or damage from pests in productive systems.

The combination of GIS with DRIS provides a better understanding of the interrelation of nutritional and non-nutritional variables on crop yield. In Brazil, these tools have identified the relationship of the incidence of rust (Hemileia vastatrix) and CBB (Hypothenemus hampei) with the NBI in terms of yield for Conilon coffee (Da Silva et al., 2020), along with the zoning of areas with a nutritional imbalance and low yield in adult Açaí palm plants (Oliveira et al., 2020). Meanwhile, in Colombia, the spatial distribution of NBI in the soil and leaf tissue of plants has led to the validation of Critical Levels and Sufficiency Ranges in oil palm (Herrera, 2015).

G. Dynamics of heavy metals

In DRIS, the NBI shows the dynamics of heavy metals from the root to the leaves of plants (biochemical and physiological events), and the identification of deficiencies or excesses of micronutrients, which are not easy to analyze with conventional diagnosis methods. In Brazil, the translocation of cadmium to the shoots has been studied in wheat, finding that the concentrations of this metal increase those of Fe and Mn in plants (Silveira et al., 2020). Similarly, Fe deficiencies have been identified in adult Satsuma mandarin trees in China (Huang et al., 2012), and the correction of excess Fe and deficiencies in Zn, Cu, and Mn in roses grown under greenhouse in Colombia (Franco-Hermida et al., 2020) has been shown.

H. Nutrition and disease incidence

Studies are based on the calculation of the NBI in plants to define the nutritional balance and correlate it with the incidence of diseases caused by fungi and bacteria. Alves and Vilela (2013) reported that banana plants in Brazil with a good potassium balance have a lower incidence of Fusarium oxyspo-rum f. sp. cubense, the highest performance, and the best economic feasibility. Likewise, Maity et al. (2016) reported tolerance and susceptibility to Xanthomonas axonopodis in pomegranate plants in India according to their nutrition level.

I. Other applications in agriculture

These uses for the DRIS system mean it is important to know the existence of other applications with less use in agronomy, which have been studied in different crops in recent years in some countries.

In Spain, for example, it has been reported that the development of DRIS norms for nutritional diagnosis in leaf limb and petiole in vines (Martín et al., 2013), and Sufficiency Ranges have been determined for leaf tissue under saline conditions in ornamental crops (García- Caparrós et al., 2019). Additionally, DRIS norms and evaluation of phytotoxicity in plants irrigated with agricultural irrigation wastewater in kale in China (Hu et al., 2016), Petiole Sap Analysis (PSA) Vs DRIS leaf analysis in tomato in Mexico (Ávila-Juárez & Rodríguez-Ruiz, 2020), and DRIS norms in seeds used as diagnostic tissue of the nutritional status for soybean in Brazil (Fávero et al., 2020) have also been found.

V. Advantages and disadvantages of DRIS

In general, this system has some advantages compared to other diagnostic methods. It has a continuous scale, is easy to interpret, and ranks nutrients in order of importance required by the plant (from the most deficient to the one with the greatest excess).

DRIS also takes into account the interaction and balance of mineral elements both in the soil and in the plant, can perform diagnosis at any stage of crop development because it is less sensitive to tissue aging, and eliminates problems associated with determining critical concentrations. Furthermore, DRIS has good potential for assessing nutrient levels under local conditions, incorporates the dry biomass of crops into the diagnosis, and eliminates limitations imposed by environmental factors (González, 2017; Herrera, 2015; Kania & Callejas, 2011; Landriscini & Galantini, 2018).

Among the evident disadvantages, DRIS requires norms, which are still scarce for various crops of agronomic interest. It also needs large volumes of information (which may be lacking in quality) to obtain DRIS norms. The data used for norms must come from units with similar agronomic management and have a yield record. Because the reliability of the generated models depends on this and automation is needed to improve adoption, DRIS is a complex method to develop (González, 2017; Herrera, 2015; Kania & Callejas, 2011).

VI. Conclusions

DRIS is a valid tool for nutritional diagnosis and requires norms for implementation. Consequently, a large database is needed at the local and/or regional level on a particular genetic material, considering climatic and relief factors, plant tissue analysis, physical and chemical properties of the soil, and agronomic management and yield to achieve reliable results that can be validated.

DRIS approaches the nutritional diagnosis of crops holistically through the relationship of the nutrient balance of plants and soil, with dynamics according to the phenological stage, identification of Critical Levels, Sufficiency Ranges, and correlation of the nutritional status with phytosanitary problems.

This system has elucidated complex processes to be interpreted with conventional diagnostic methods such as the dynamics of heavy metals in plants, phytotoxicity in plant tissue irrigated with wastewater from agricultural irrigation, Sufficiency Ranges in leaf tissue under saline conditions, analysis of sap, and evaluation of the nutritional status from seeds used as diagnostic tissue.

Currently, the modified DRIS (M-DRIS) takes into account the dry biomass of crops, as an additional constituent for the calculation of indices, which have been used in species such as alfalfa and artichoke.

Acknowledgments

Thanks to the Colombian Agricultural Research Corporation-AGROSAVIA for granting the study permit under the modality of "Free Master" for Jorge Enrique Villamil Carvajal and to Juan Felipe Ossa Yepes for translating the manuscript

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