Prescient Diagnostic Analysis for boron nutritional status in soy crops

doi:10.21203/rs.3.rs-1755230/v1

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Prescient Diagnostic Analysis for boron nutritional status in soy crops

https://doi.org/10.21203/rs.3.rs-1755230/v1

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published 09 Feb, 2023

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Boron is the most limiting micronutrient for soybean yield; therefore, accurate identification of its nutritional status is important for adequate fertilization management and maximize soybean yield potential. Currently, tools for nutritional status interpretation of B, such as the CND and DRIS are used; however, their efficacy is not considered to identify the true nutritional status of B. Here, we investigated the efficacy of these methods to identify the nutritional status of B in 140 commercial soybean crops to obtain nutritional standards for the DRIS and CND methods. In addition, an experiment of B dose calibration (0, 300, 600, 1200 and 1800 g ha^-1) was installed to assess the quality of nutritional diagnoses using the PDA. We tested the limits of 0.25, 0.50, and 1.00 for the NRr and values of 1%, 5%, or 10% for YR. The DRIS method was more effective and, on average, its variations increased yield by 27% compared to CND, with the best performance of DRIS when NRr = 1.00 was adopted with 10% for YR. This study highlights the need for reliable and accurate diagnostic methods with global implications for crop sustainability by improving the efficacy of B fertilization programs and crop yield.

Foliar diagnosis

Glycine max L.

boron fertilization

accuracy

DRIS

CND

Acidic soils with low organic matter (OM) contents have boron (B) as one of the micronutrients that most limit soybean yield [1]. In soybean, high yields and good quality seeds require B fertilization [2, 3]. Foliar spray is the most used technology for B fertilization in soybean crops[4, 5, 6]. This type of fertilization meets the soybean crop demand for B because the amounts are relatively low and, as B is a micronutrient, its absorption via foliar spray is relatively high [7].

Numerous studies have been conducted on soybean crops [4, 8, 9, 10, 11, 12, 13] with emphasis on diagnostic methods based on nutritional balance, such as the Diagnosis and Recommendation Integrated system (DRIS) and the Compositional Nutrient Diagnosis (CND).

Numerous advances have been achieved over the years, such as (a) selection of bivariate ratios to calculate DRIS indices by the Variance ratio (DrisVar) [14]; (b) the F-Test (DrisFtest) [15]; (c) adoption of direct and indirect relationships [16]; (d) use of the Logarithmic ratio transformation (DrisLog) [17] (e) use of multivariate relationships [18]; and (f) nutrient responsiveness (NR) criterion [19], all aimed at achieving greater efficacy of the prognoses produced.

On the other hand, evaluation of the efficacy of these methods primarily compares the degree of agreement of the diagnoses produced with each other or by comparing the diagnoses with those from the conventional methods. Nevertheless, this type of assessment presents a significant distortion, as a high agreement degree could be obtained between criteria with low effectiveness to identify true deficiencies [20, 21]. Therefore, studies are needed for more efficient diagnostic methods.

The assessment of the true nutritional status based on the positive response of crops to the correction of deficiencies has been neglected. Studies are restricted to a few species, such as the works on soybean [22], banana [23], eucalyptus seedlings [24], and sugarcane [20, 25], which adopted the Prescient Diagnostic Analysis (PDA) recommended by to assess the quality of nutritional diagnoses [26].

Many studies have used these diagnostic methods in soybean [8, 9, 10, 11, 12, 13] nevertheless, these methods were not subjected to accuracy tests. The use of low-accuracy methods may produce inaccurate diagnoses, constituting a major limitation to optimize the use of fertilizers.

The science of accurate nutritional diagnoses in crops should advance, due to the high cost of fertilizers and the finite nature of crops, which requires precise tools for the decision-making regarding fertilization to ensure economic return of the agricultural activity.

Here, we hypothesize that adjustments applied to a diagnostic method improves the quality of diagnoses. Therefore, possible adjustments to the DRIS method should outperform the CND method in predicting diagnoses for B status in soybean.

This research investigated the most effective method to diagnose the nutritional status of B in soybean, comparing the CND method with variations of the DRIS method. We aimed to identify the best tool B diagnoses in soybean, enhancing its use, while increasing sustainable crop yield.

The nutrient responsiveness range (NRr) was tested ranging from 1.00 to 0.25. As NRr reduced, the frequency of cases of diagnosis of nutritional deficiency for B increased for all diagnostic methods tested. This increase in the deficiency frequency of cases was lower for the DrisVar and DrisFtest methods, intermediate for CND and DrisLog methods, and higher for the DrisAll method (Table 1). The DrisAll was the most sensitive method to changes in the NRr value, ranging from 0 to 25% of cases of B deficiency, while the DrisVar and DrisFtest were the least sensitive to changes in NRr (Table 1).

The difference between the different diagnostic methods was significant as the NRr was reduced. For NRr = 1.00 only the CND method differed from the others, while NRr = 0.25 showed the greatest difference in the capacity of the methods to identify cases of B deficiency.

Applying the comparison between the diagnoses for the entire set of soybean crops, with the exception of the DrisLog method, all the other methods presented PG above 90%, with NRr = 1.00 (Table 2). The main trend was the PG reduction as the NRr also reduced. The biggest difference for PG was 21%, when NRr ranged from 1.00 to 0.25 for the comparison between the DrisFtest and DrisVar methods. The smallest difference was 1% in the PG when the NRr ranged from 1.00 to 0.25 in the comparison between the CND and DrisVar methods (Table 2).

Methods with similar results regarding the ability to indicate B-deficient soybean crops showed different behavior when compared to PG, such as in the comparison between CND and DrisLog or between DrisFtest and DrisVar. On the other hand, different methods showed higher PG regarding the capacity to indicate B-deficient soybean crops, such as the comparison between DrisAll and DrisVar (Table 2).

The dispersion of yield values was greater for low NBIa values (Figures 1a and 2a) or for B-index close to zero (Figures 1b and 2b), for all soybean crops (Figure 1), or experimental crops (Figure 2). For higher NBIa values, or B-index far from zero, the yield values of soybean crops showed lower dispersion and were equal to or above the average of the data set evaluated (Figures 1 and 2)

The B-index values for crops without B application (0 kg ha^-1 of B) were negative (Figure 2b). The B application showed positive B-index values, tending towards nutritional excess at the highest doses (1,200 to 1,800 kg ha^-1 of B) (Figure 2b). The same effect was verified for the NBIa x yield ratio: when increasing the applied B dose, higher values were obtained for NBIa (Figure 2a).

The AccDef and DR indicators diverged between the diagnostic methods as the NRr was reduced from 1.00 to 0.25 and the YR from 10% to 1%; however, this divergence between the methods was practically annulled when the YR of 10% and NRr of 1.00 (Table 3).

The AccSuf and SR indicators differed slightly between the diagnostic methods with the best performance for these indicators achieved with YR of 10% and NRr of 1.00 (Table 03).

The CND method was the most efficient to identify TDef cases and associated to the lowest number of FSuf resulted in higher rates for the DR indicator and better AccDef, mainly for NRr of 0.25 and YR of 1%. The DRIS methods showed a better performance for DR and AccDef measurements with NRr of 0.25 and YR of 5% (Table 3).

The net yield response (Net d(Y)) was negative for NRr of 1.00 and YR = 1% or 5%. The CND method showed positive Net d(Y) only with YR of 10% and NRr of 0.50 (Figure 3). Most variations of the DRIS method showed positive Net d(Y) with YR = 10%, except for DrisAll and DrisLog when NBr was 0.25 (Figure 3).

Taking the Net d(Y) indicator as a reference for comparing the diagnostic methods, variations in the DRIS method resulted, on average, in a Net d(Y) 27% higher than that achieved by the CND (Figure 3).

The diagnoses obtained by the DRIS and CND methods for B diagnosis in soybean presented a medium-to-high PG (68 to 95%). Also was observed a high PG (90%) for the DRIS and CND in the nutritional B diagnosis in soybean [10]. However, a high PG does not ensure that the interpretation of nutritional status is correct, as it only indicates that the interpretation methods are producing coincident diagnoses [20, 27].

In sugarcane, a higher PG did not always correspond to greater yield gains through the use of nutritional diagnosis [20, 25].

Yield dispersion data, based on the NBIa and B-index, for the set of crops monitored (Figure 1) or just for the experimental crops were not good indicators to evaluate the performance of the diagnostic method, as exemplified for the DrisAll method.

The theoretical model predicted for this dispersion consists of a greater range of yield values for NBIa low values and a smaller range of yield values for NBIa high values [28], which was not observed for commercial or experimental crops.

Was found similar results in a study conducted under experimental conditions, with P application in sugarcane, reporting the occurrence of crops of medium and high yields in situation of plants with greater nutritional imbalance [20]. This may be associated to the premise that the nutrient alone is not the only determining factor in plant yield and that other factors, nutritional or not, may be the precursors of yield gains [7].

Analysis of Diagnosis Prediction

The increase in the limit adopted to define the YR reduced the probability of the crop to be considered as having a true B deficiency. Also was found a lower frequency of sugarcane plants in a state of nutritional deficiency for P, as the YR value ranged from 5% to 40% [20].

The threshold defined for YR directly affected the accuracy indicators. Regardless of the diagnostic method evaluated, the best performance was observed when the limit of 10% was adopted for YR. Conversely, to sugarcane was attributed the higher efficiency of diagnosis by the CND method to the YR values of 35% and 40% [20]. This difference is explained by the greater responsiveness of nutrient P in sugarcane growth with yield gains between 30% and 35% [29, 30]. On the other hand, for B application in soybean, the plant response varied from null [31, 32] to a maximum of 8% to 17% [2, 3].

The DR indicator was ineffective to evaluate the performance of B diagnostic methods in soybean, as DR above 1, suggested as adequate [22], resulted in a lower net yield response. Also was observed a low RD to evaluate the CND method, adopting NRr = 0.00, even though this was the diagnostic method with the best performance to assess the nutritional P status in sugarcane [20]. To eucalyptus seedlings was found that the highest RD proportions were achieved by the most efficient method in the nutritional diagnosis, which varied according to the nutrient evaluated [24].

Similarly, was reported that the CND method showed low efficiency to identify the sugarcane nutritional status [20]. According to the authors, the lower efficiency of the CND can be explained by the low sensitivity of the method in making fine adjustments to the nutritional status or by the low assertiveness when greater yield gains are desired, given that positive Net Yield Responses (NYR) were observed only for YR above 20-30% and NRr = 0.00 [20].

The SR indicator was promising to evaluate plants considered in nutritional balance, as higher SR proportions were accompanied by a higher NYR.

For the DRIS method, the limit of NRr =1 resulted in a better performance to identify the true B nutritional status in soybean. On the other hand, the limit of NRr = 0.25 showed a larger number of cases of false deficiency, compromising the primary objective of nutritional diagnosis to correctly identify nutritional deficiencies [22]. In this study, the better performance of DRIS (especially when using YR=10% and NRr=1) was mostly attributed to the greater sensitivity of the method to correctly identify cases of nutritional sufficiency, which may be a consequence of the lower responsiveness of soybean to boron fertilization [31, 32].

In general, for a less responsive nutrient such as B, a lower value for NRr (aiming to increase the frequency of nutritional deficiency diagnoses) proved to be ineffective to identify the TNS of B in soybean. Also was reached unsatisfactory results for the nutritional diagnosis of N, P, and K in soybean [22]. As the author obtained low accuracy, they adopted NRr = 0 (every nutrient with a negative DRIS index was considered deficient). Conversely, in a study on sugarcane with a responsive nutrient, such as P, Silva et al. (2021a) concluded that increasing the probability of P deficiency responses (NRr =0) resulted in better NYR.

As the DRIS has several variations in its application, these results point to the need to perform an accuracy test to improve nutritional diagnosis. For example, DrisFTest was significant for presenting the highest yield in two different data adjustments (NRr = 0.50 with 10% for YR and, NRr = 1.00 with 10% for YR), even with a smaller increment of cases of disability diagnoses.

Our hypothesis that the adjustments made in the diagnostic procedures was true. The methodological variations introduced in the DRIS method not only provided better accuracy to this method than that obtained by the CND method, as the variations applied to the diagnostic process also affected the quality of the diagnostics produced.

We carried out assays of foliar B and nutritional monitoring of commercial soybean crops in experimental and commercial production sites, respectively, in the municipality of Chapadão do Sul, Mato Grosso do Sul State, Brazil. The dystrophic Red Latosol predominates in the experimental site and in most of the municipality [33]. The climate in the region is humid tropical (Aw) (Köppen classification) with a two-month dry season and an average annual rainfall of 1550 mm [34].

The nutritional status was monitored in the 2015/2016 harvest of commercial crops, in a no-tillage system in straw, but with different phytotechnical managements. We used cultivars P98Y30, W791, M7739, CANCHEIRO, TEC7849, N7737, DESAFIO, M9144, SYN1288, BG4184, M7339 IPRO, AS3797 IPRO, M8210 IPRO, NS7670, and 98Y52. In each crop, a perimeter of one hectare was delimited for leaf sampling. Yield was estimated in kg per hectare of soybean grain, adjusted for water content in the grain at 13%. We were having licenses to collect soy crop.

This research was not conducted with endangered species and all methods in this study were carried out in compliance/accordance with relevant institutional, national, and international guidelines and legislation.

The foliar B fertilization test (calibration test) was designed to determine the B response curve on crop yield as well as leaf B levels in soybean cultivar DESAFIO. The soil of the experimental site presented 0.32 mg B dm^-3 with extraction by hot water, a content interpreted as low [35]. The experimental design was randomized blocks, with five B doses (0, 300, 600, 1200, and 1800 g ha^-1), applied as boric acid, with five replications per treatment.

In pre-sowing, 100 kg ha^-1 of KCl was applied like broadcast application. At sowing, on Nov 24, 2015, we applied 115 kg ha^-1 of monoammonium phosphate (N:11% and P₂O₅:52%). The total experimental site comprised 852.5 m², divided into 25 experimental plots of 11.0 m long and 3.1 m wide. Each experimental plot consisted of seven planting rows, where the three central lines were the useful area for the evaluations with eight central meters used per row.

Boron was applied via foliar with a CO₂ pump sprayer, adjusted to a spray volume of 150 L ha^-1. To increase B absorption, 0.15% surfactant (nonionic polyoxymethylene surfactant) and 1% urea were mixed with the syrup. Each dose was divided into three applications, two in the vegetative phase (V2 and V5) and one at the beginning of flowering (R1). The applications were carried out in the morning at a temperature near 25ºC, relative humidity of about 80%, and wind speed near 7 km h^-1.

Soybean leaves were sampled in the experimental site at stage R1 (beginning of plant flowering) at 10 days after the last B application. Each sampling site was represented by the random collection of 25 fully expanded leaves, from the third trifoliate, with petiole, counted from the plant apex [36].

The leaves sampled in the calibration test plots and in the commercial crops, containing the petiole, were washed in deionized water, in a detergent solution (0.1% v/v), and then rinsed with hydrochloric acid solution (0. 3% v/v) and deionized water. Subsequently, the leaves were dried in a forced circulation oven at 60-70 °C until reaching constant mass and then ground in a mill.

The nutrient contents were analyzed to different digestion processes [37]: microwave (K), sulfuric (N) and nitroperchloric (P, S, Ca, Mg, Mn, Fe, Zn, and Cu). After digestion, the leaf samples were analyzed to determine the contents of S, Ca, Mg, Mn, Fe, Zn, Cu (ICP-OES plasma spectrometry), K (flame photometry), and P (molecular spectrophotometry). Total N was determined by Kjehdahl distillation. For each nutrient, we identified leaf samples with nutrient contents within ± 95% range of the mean in the 165 samples data set (was used data set of the soybean yield and B contents in experimental plots and commercial crops).

The harvesting of in commercial crops farms was mechanized and carried when the plants was reached full maturation. In the experimental site, it took place on 05 april 2016, obtaining the productivity (Bags ha^-1) of the plots. The bag is equivalent to 60 kg of grain. We were having licenses to collect soy crop.

The intermediate functions to generate the nutritional indices for the DRIS were calculated following the formula originally [28] and by the original Beaufils formula with logarithmic transformation of the relationships between nutrients (DRIS Logarithmic ratio - DrisLog) [17].

For both formulas to calculate the nutrient balance index by the DRIS method, the F-Test criterion [15] was used to select the direct and inverse relationships between the nutrients to compose the calculation of the DRIS indices, called the “DRIS F-Test (DrisFtest)”.

For the first case, when there was no data transformation [28], the variance ratio criterion [14] was also used, here called the “DRIS Variance ratio (DrisVar)”

The use of all nutritional ratios was an alternative tested, without any selection [16], called “DRIS All ratios (DrisAll)”.

To apply the DrisFtest and DrisVar criteria, the F value was calculated by the ratio between the variance (S²) of subpopulations from low to high yield for the direct and inverse relationships between nutrients. The F tabulated was obtained based on the degrees of freedom by the number of crops in the low- and high-yield population minus one, at a significance level of 5%. Samples with yield above the mean +0.25 of standard deviation were considered high-yielding populations.

We used the samples that contained the 11 nutrients to obtain nutritional standards by the CND method (norms), determining the means of the multivariate relationships of high yield [18]. After obtaining the CND norms, the nutrient balance index was obtained [10].

The criterion of the nutrient responsiveness method (NR) was used to interpret the nutrient balance index obtained by the DRIS and CND methods through the Average Nutrient Balance Index (NBIa) (IBNm) [19], which consists of grouping the boron balance index (B-index) into two classes: insufficient when the B-index was negative and, in module, lower than the f x IBNm (nutrient responsiveness range – NRr). In all other cases, the B-index was considered balanced. The values of 0.25, 0.50, and 1.00 for f were used.

We carried out the nutritional diagnosis of the experimental plots using the DRIS and CND methods, classifying the plots as B deficient or B sufficient. The accuracy of correctness of these diagnoses was performed by the APD procedure [26] by comparing the diagnosis given by the diagnosis method (DRIS and CND) with soybean true nutritional status (TNS).

The TNS is obtained by analyzing the soybean yield response in each experimental plot as a function of B application. Comparing a plot with B application with another without B application or with B applied at smaller doses. When the Yield Response (YR) was of at least 1%, 5%, or 10% in soybean yield showed that the plot was truly B deficient/insufficient. In all other cases, the plot was considered to be truly adequate/balanced.

The deficiency diagnosis of the methods (DRIS and CND) was considered true (TDef) when there was a yield increase with the nutrient addition and false (FDef) when there was no yield increase. The sufficiency diagnosis of the methods (DRIS and CND) was considered true (TSuf) if B application did not increase yield and false (FSuf) if there was a yield increase (Table 4) [26].

The quality of predictions was then quantified by different indicators, namely accuracy (Acc), net yield response (Net d(Y)) [26], deficiency ratio (DR), accuracy for deficiency (AccDef), and accuracy for sufficiency (AccSuf) (BEVERLY, 1993). A new calculation was introduced for the accuracy test, which is the ratio of the sufficiency radius (SR). The diagnostic quality indicators were created according to the number of counts of indicated categories [22, 26].

The Acc is given by the percentage sum the of true diagnosis cases and obtained by the “equation (1)”, where n is the total number of comparisons performed.

Acc = 100* (TDef/n + TSuf/n) (1)

AccDef and AccSuf correspond to the percentage of correct answers in relation to the total of deficiency or sufficiency diagnoses, respectively, and were obtained by the “equation (2) and (3)”, where VDef and ∑Suf are the sum of cases of deficiency and sufficiency, respectively.

AccDef = 100 x TDef / ∑Def (2)

AccSuf = 100 x TSuf/ ∑Suf (3)

The DR was calculated by the ratio of true deficiency for false deficiency diagnoses and the SR was calculated by the ratio of true sufficiency for false sufficiency diagnoses, they was obtained by the “equation (4) and (5)”, respectively.

RD=%TDef/%FDef (4)

SR=%TSuf/%FSuf (5)

Net d(Y) = |P_VDEF| + |P_VSUF| - |P_FDEF| - |P_FSUF| (6)

The Parity Grade (PG) was also measured between the different diagnoses of the nutritional status of B provided by each of the diagnostic methods used. Between these methods, the true nutritional status was determined experimentally. The PG was calculated by the frequency of cases with the same diagnoses (in agreement between the methods) in relation to the total of diagnoses compared with each other.

Then we conclude that the DRIS was more promising than the CND method to diagnose the B nutritional status in soybean, provided the nutrient responsiveness range value and the criterion to define YR be adjusted.

Author contributions

E.I.F.T. contributed to the collected data, to the analysis of data and creation of graphsand and to the manuscript writing and revision. P.G.S.W. contributed to contributed to the analysis of data and to the manuscript writing and revision. R.M.P. contributed to the manuscript writing and revision. C.G.R. conducted the experiments. CRW conducted the experiments and collected the data.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The author(s) declare no competing interests.

Additional information

Correspondence and requests for materials should be addressed to E.I.F.T.

Tomicioli, RM, Leal, FT, & Coelho, AP. Limitação da produtividade pela deficiência de boro nas culturas da soja, milho, feijão e café.J South Am Earth Sci. 2(1), e21100; 17648/sas.v2i1.100 (2021).
Valerevich, AS. О возможности диагностирования потребности растений сои в сере, молибдене и боре для эффективной некорневой подкормки Научный журнал КубГАУ. 110,1072-1089; http://ej.kubagro.ru/2015/06/pdf/71.pdf (2015).
Longkumer, LT, Singh, AK., Jamir, Z., & Kumar, M. Effect of sulfur and boron nutrition on yield and quality of soybean (glycine max L.) grown in an acid soil Commun Soil Sci Plant Anal. 48(4), 405-411; 1080/00103624.2016.1269788 (2017).
Santos, M.; Cerutti, PH., & Wille, CL. Adubação foliar com boro em sistema de plantio direto na cultura da soja Cient. Rural. 21(1),1-11; http://revista.urcamp.tche.br/index.php/RCR/article/view/346/pdf_1 (2019).
Varanda, MAF, Menegon, MZ, Nascimento, VL, Capone, A, Barros, HB. Efeitos da aplicação foliar de boro na produtividade de soja na várzea irrigada. Pesq. Aplic. Agrotec, .11(2),15-22, 5935/PAeT.V11.N2.02. https://revistas.unicentro.br/index.php/repaa/article/viewFile/5189/3677 (2018).
Coutinho Neto, A, Silva, GP, Coutinho, ELM & Prado, RM. Foliar sources of boron and manganese in soybean and zinc plants in corn plants complexed with polyols in nutritional status and in production Idesia 38, 97-105; 4067/S0718-34292020000300097 (2020).
Prado, RM. Mineral Nutrition of Tropical Plants (ed Prado, RM) 279-312. 1007/978-3-030-71262-4 (Springer International Publishing, 2021).
Harger, N, Fioretto, R & Ralisch, R. Avaliação nutricional da cultura da soja pelos métodos DRIS e Níveis de Suficiência Semin Cienc Agrar 24, 219-224; 5433/1679-0359.2003v24n2p219 (2003).
Maeda, S, Ronzelli Júnior, P & Lucchesi, LAC. Valores de referência do DRIS para a soja, cv. CD 202, para a região sul do Mato Grosso do Sul, Brasil Agrar. 5, 1-2, 10.5380/rsa.v5i1.1094 (2004).
Urano, EOM et al. Determinação de teores ótimos de nutrientes em soja pelos métodos Chance Matemática, Sistema Integrado de Diagnose e Recomendação e Diagnose da Composição Nutricional Bras. Ci. Solo 31, 63-72; 10.1590/S0100-06832007000100007 (2007).
Castamann, A, Escosteguy, PAV, Berres, D & Zanella, S. Diagnosis and recommendation integrated system (DRIS) of soybean seed oil content. Bras. Ci. Solo 36, 1.820-1.827; 10.1590/S0100-06832012000600016 (2012).
Kurihara, CH, Venegas, VH, Neves, JCL., Novais, RF & Staut, LA. Faixas de suficiência para teores foliares de nutrientes em algodão e em soja, definidas em função de índices DRIS. Ceres 60,412-419; 10.1590/S0034-737X2013000300015 (2013).
Souza, HAD et al. Critical levels and sufficiency ranges for leaf nutrient diagnosis by two methods in soybean grown in the Northeast of Brazil Bras. Ci. Solo 44; 10.36783/18069657rbcs20190125 (2020).
Walworth, JL & Sumner, ME. The diagnosis and recommendation integrated system (DRIS). Soil Sci. 6,149-188; 10.1007/978-1-4612-4682-4_4 (1987).
Jones, CA. Proposed modifications of the diagnosis and recommendation integrated system (DRIS) for interpreting plant analyses in Soil Sci. Plant Anal 12,. 785-794, 10.1080/00103628109367194 (1981).
Alvarez, VH & Leite, RA. Fundamentos estatísticos das fórmulas usadas para cálculo dos índices DRIS. Bras. C. Solo Bull 24, 20-25 (1999).
Beverly, RB. Modified DRIS method for simplified nutrient diagnosis of ‘Valencia’ oranges. Plant Nutr. 10, 1.401-1.408; (1987).
Parent, LE & Dafir, MA. Theoretical concept of compositional nutrient diagnosis. Amer. Soc. Hort. Sci. 117, 239-242; 10.21273/JASHS.117.2.239 (1992).
Wadt, PGS. Relationships between soil class and nutritional status of coffee plantations. Bras. Frutic. 29, 227-234; 10.1590/S0100-06832005000200008 (2005).
Silva, GP et al. Accuracy of plant response potential to fertilization in nutritional diagnoses for phosphorus in sugarcane J Plant Nutr. 45, 1-10; 1080/01904167.2021.2014869 (2021).
Traspadini, EIF et al. Efficiency of critical level and compositional nutrient diagnosis methods to evaluate boron nutritional status in soybean. J. Agric. Res. 82(2), 309-319; 10.4067/S0718-58392022000200309 (2022).
Beverly, RB. DRIS diagnoses of soybean nitrogen, phosphorus, and potassium status are unsatisfactory. J Plant Nutr. 16(8),1431-1447; 1080/01904169309364625 (1993).
Teixeira, LAJ, Santos, WD, Bataglia, OC. Diagnose nutricional para nitrogênio e potássio em bananeira por meio do Sistema Integrado de Diagnose e Recomendação (DRIS) e de Níveis Críticos. Rev Bras. Frutic. 24, 530-53; 1590/S0100-29452002000200050 (2002).
Morais, TCB et al. Efficiency of the CL, DRIS and CND Methods in assessing the nutritional status of Eucalyptus rooted cuttings. Forests 10(9), 786; 10.3390/f10090786 (2019).
Silva, GP et al. Accuracy of nutritional diagnostics for phosphorus considering five standards by the method of diagnosing nutritional composition in sugarcane. J Plant Nutr. 1:1-14; 1080/01904167.2020.1730902 (2020).
Beverly, RB & Hallmark, WB. Prescient diagnostic analysis: a proposed new approach to evaluating plant nutrient diagnostic methods. Communications in Soil Science and Plant Analysis 23(17/20):2633-2640;1080/00103629209368761 (1992).
Azevedo, JMA, Wadt, PGS, Pérez, DV, & Dias, JRM. Normas DRIS preliminares para pupunheira cultivada em diferentes sistemas de manejo na região sul-ocidental da Amazônia. Rev Agroamb. 10(3), 183-192; 18227/1982-8470ragro.v10i3.3253 (2016).
Beaufils, E. R. Diagnosis and recommendation integrated system (DRIS) Univ. Soil Sci. Bull 1, 132 (1973).
Aziz, A, Ahiabor, BDK, Opoku, A & Abaidoo, RC. Contributions of rhizobium inoculants and phosphorus fertilizer to biological nitrogen fixation, growth and grain yield of three soybean varieties on a fluvic luvisol J. Exp. Agric. 10(2), 1-11; 10.9734/AJEA/2016/20072 (2016).
Antonangelo, JA et al. Soybean yield response to phosphorus fertilization in an Oxisol under long‐term no‐till management Soil Sci Soc Am J. 83(1), 173-180; 2136/sssaj2018.07.0251 (2019).
Seidel, EP, Egewarth, WA, Piano, JT, and Egewarth, J. Effect of foliar application rates of Calcium and Boron on yield and yield attributes of soybean (Glycine max) J. Agric. Res.10(4),170-173; 10.5897/AJAR2014.9046 (2015).
Enderson, JT, Mallarino, AP, & Haq, MU. Soybean yield response to foliar-applied micronutrients and relationships among soil and tissue tests. Agron 107(6), 2143-2161; 10.2134/agronj14.0536 (2015).
IBGE. Instituto Brasileiro de Geografia e Estatística. Mapa de Solos do Brasil. https://geoftp.ibge.gov.br/informacoes_ambientais/pedologia/mapas/brasil/solos.pdf (2001).
Mapa de clima do Brasil. Instituto Brasileiro de Geografia e Estatística (IBGE), Rio de Janeiro, Brasil. http://geoftp.ibge.gov.br/informacoes_ambientais/climatologia/mapas/brasil/Map_BR_clima_2002.pdf (2002).
Souza, DMG & Lobato, E. Correções do Solo e Adubação. (2ª ed. Brasília) 318-326 (Embrapa Informação Tecnológica, 2004).
Malavolta, E. Manual de Nutrição Mineral de Plantas.568-605 (Ceres, 2006).
Carmo, CDS, Araujo, WS., Bernardi, ADC & Saldanha, MFC. Métodos de análise de tecidos vegetais utilizados na Embrapa Solos. 41, Circular Técnica nº6 https://www.infoteca.cnptia.embrapa.br/handle/doc/337672 (Embrapa Solos, 2000).

Table 1. Percentage of deficiency diagnosis interpreted by the DRIS and CND methods, as a function of the nutrient responsiveness range (NRr) for B diagnosis in soybean, cultivated in Chapadão do Sul, Mato Grosso do Sul, Brazil.

Methods¹	NRr
	1.0	0.50	0.25
	------------------------------ % ------------------------------
DrisAll	0	5	25
DrisFtest	0	0	5
DrisVar	0	5	5
DrisLog	0	5	20
CND	5	15	20

1 Methods DRIS adopting the criteria of selecting all bivariate relations - DrisAll, using the criterion Ratio of Variances - DrisVar, using the F test - DrisFtest and using the logarithmic transformation - DrisLog and compositional nutrient diagnosis - CND.

Table 2. Parity Grade (PG) obtained between the diagnoses of the DRIS and CND methods adopting different range factors adopted in the nutrient responsiveness range (NRr) for the diagnosis of B in soybean, cultivated in Chapadão do Sul, Mato Grosso do Sul, Brazil.

Degree of agreement (%)
		DrisAll	DrisFtest	DrisVar	DrisLog	CND
DrisAll	1.00	-
	0.50	-
	0.25	-
DrisFtest	1.00	93	-
	0.50	81	-
	0.25	75	-
DrisVar	1.00	95	96	-
	0.50	85	88	-
	0.25	75	88	-
DrisLog	1.00	85	84	83	-
	0.50	72	64	72	-
	0.25	79	63	70	-
CND	1.00	91	95	92	84	-
	0.50	79	85	84	69	-
	0.25	79	84	91	68	-

DRIS method adopting the criteria of selecting all bivariate relations - DrisAll, using the criterion Ratio of Variances - DrisVar, using the F test - DrisFtest and using the logarithmic transformation - DrisLog and compositional nutrient diagnosis - CND.

Table 3. Percentage of identified cases of diagnoses true deficiency (TDef), false deficiency (FDef), true sufficiency (TSuf), false sufficiency (FSuf); accuracy (Acc); deficiency ratio (DR), sufficiency ratio (SR) accuracy for deficiency (AccDef), accuracy for sufficiency (AccSuf) obtained by the diagnosis of the DRIS and CND methods as a function of the nutrient responsiveness range (NRr) and yield response (YR) of the nutritional status of foliar boron in a soybean cultivar, in Chapadão do Sul, Mato Grosso do Sul, Brazil.

Method	NRr	YR	VDef	FDef	VSuf	FDef	Acc	AccDef	AccSuf	DR	SR
		----------------------------------------%------------------------------------------
DrisAll	1.00	1	0	0	40	60	40	0	100	0.0	0.7
		5	0	0	55	45	55	0	100	0.0	1.2
		10	0	0	80	20	80	0	100	0.0	4.0
	0.5	1	5	0	40	55	45	8	100	1.0	0.7
		5	5	0	55	40	60	11	100	1.0	1.4
		10	0	5	75	20	75	0	94	0.0	3.7
	0.25	1	15	10	30	45	45	25	75	1.5	0.7
		5	15	10	45	30	60	33	82	1.5	1.5
		10	0	25	55	20	55	0	69	0.0	2.7
DrisVar	1.00	1	0	0	40	60	40	0	100	0.0	0.7
		5	0	0	55	45	55	0	100	0.0	1.2
		10	0	0	80	20	80	0	100	0.0	4.0
	0.5	1	5	0	40	55	45	8	100	1.0	0.7
		5	5	0	55	40	60	11	100	1.0	1.4
		10	0	5	75	20	75	0	94	0.0	3.7
	0.25	1	5	0	40	55	45	8	100	1.0	0.73
		5	5	0	55	40	60	11	100	1.0	1.4
		10	0	5	75	20	75	0	94	0.0	3.7
DrisFtest	1.00	1	0	0	40	60	40	0	100	0.0	0.7
		5	0	0	55	45	55	0	100	0.0	1.2
		10	0	0	80	20	80	0	100	0.0	4.0
	0.5	1	0	0	40	60	40	0	100	0.0	0.7
		5	0	0	55	45	55	0	100	0.0	1.2
		10	0	0	80	20	80	0	100	0.0	4.0
	0.25	1	5	0	40	55	45	8	100	1.0	0.7
		5	5	0	55	40	60	11	100	1.0	1.4
		10	0	5	75	20	75	0	94	0.0	3.7
DrisLog	1.00	1	0	0	40	60	40	0	100	0.0	0.7
		5	0	0	55	45	55	0	100	0.0	1.2
		10	0	0	80	20	80	0	100	0.0	4.0
	0.5	1	5	0	40	55	45	8	100	1.0	0.7
		5	5	0	55	40	60	11	100	1.0	1.4
		10	0	5	75	20	75	0	94	0.0	3.7
	0.25	1	10	10	30	50	40	17	75	1.0	0.6
		5	10	10	45	35	55	22	82	1.0	1.3
		10	0	20	60	20	60	0	75	0.0	3.0
CND	1.00	1	0	0	37	63	37	0	100	0.0	0.6
		5	0	0	53	47	53	0	100	0.0	1.1
		10	0	0	79	21	79	0	100	0.0	3.7
	0.5	1	5	5	32	58	37	8	86	1.0	0.5
		5	5	5	47	42	53	11	90	1.0	1.1
		10	5	5	74	16	79	25	93	1.0	4.6
	0.25	1	11	5	32	53	42	17	86	2.0	0.6
		5	11	5	47	37	58	22	90	2.0	1.2
		10	5	11	68	16	74	25	87	0.5	4.3

Table 4. Diagnosis of the nutritional status obtained by the interpretation method and the physiological true nutritional status (TNS) of the crop.

Number of cases
Nutritional status interpretation	True nutritional status (TNS).
	Responsive	Non responsive
Deficient	TDef	FDef
Sufficient	FSuf	TSuf
Subtotais	∑Def	∑Suf

TDef true deficiency, FDef false deficiency, FSuf false sufficiency, TSuftrue sufficiency, ∑Def sum of deficiency, ∑Suf sum of sufficiency.

No competing interests reported.

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Prescient Diagnostic Analysis for boron nutritional status in soy crops

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