The CND-Goiaba 1.0 Software for Nutritional Diagnosis of Guava (Psidium guajava L.) Paluma, in Brazil

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1 The CND-Goiaba 1.0 Software for Nutritional Diagnosis of Guava (Psidium guajava L.) Paluma, in Brazil D.E. Rozane 1, W. Natale 2, L.E. Parent 3 and E.M.H. dos Santos 1 1 Unesp - Universidade Estadual Paulista, Campus Registro, Street: Nelson Brihi Badur, 430, CEP: , Registro, São Paulo, Brazil 2 Unesp - Universidade Estadual Paulista, Campus Jaboticabal, Via de Acesso Paulo D. Donato Castelane, km 5. CEP: , Jaboticabal, São Paulo, Bolsista PQ do CNPq, Brazil 3 ERSAM, Department of Soils and Agrifood Engineering, Université Laval, Québec (Qc), G1V 0A6, Canada Keywords: CND, computer program, leaf analysis, mineral nutrition Abstract Computer programs enable the transformation of raw data into useful information for decision making in many fields, including agriculture. Various programs have been developed to assist farmers to make better decisions about crop management practices and plant nutrition parameters. This article introduces the CND-Goiaba 1.0 software (C Sharp) and its use as a tool to perform the mathematical calculations involved in determining the compositional nutrient diagnosis (CND) indexes for the guava tree. This program was developed in Brazil, the world s leading producer of red guavas. A database was created based on 205 leaf samples collected in commercial plots (sampling units) of cultivated Paluma guava trees (Psidium guajava L.) with ages between 5 and 20 years, during the and growing seasons. The production data were normally distributed according to the Shapiro-Wilk test (W=0.988; p=0.11). The software made it possible to diagnose that 63% of the orchards evaluated needed to improve the nutritional status of their trees. The CND method showed severe nutritional imbalances in Mg and Zn in these orchards. INTRODUCTION The guava tree has the potential to produce fruits throughout the year if the orchard management considers the nutritional requirements and proper pruning and irrigation (Rozane et al., 2009). The state of São Paulo is the main guava producer in Brazil, with particular concentration in the Vista Alegre do Alto region (LUPA, 2008). However, the poor nutrient levels in tropical soils, combined in some cases with the empirical use of fertilization by farmers, causes nutritional imbalances and limiting productivity. These imbalances can be measured by foliar diagnosis, allowing the establishment of rational fertilization programs to increase harvests and reduce costs and risks of environmental contamination. Foliar diagnosis started in the mid-twentieth century (Lagatu and Maume, 1934a,b) and since then many studies have presented it as the main tool to boost crop yields. It permits efficient application of fertilizers, enabling adjustment to fit the lower or upper limits for specific nutrients and establish better overall nutrient balance for a determined crop. The results of chemical analyses of plant tissue can be interpreted by various data interpretation methods, particularly univariate and bivariate methods. More recently multivariate methods have come to the fore, such as compositional nutrient diagnosis (CND), proposed by Parent and Dafir (1992). This method relies on the relations between the concentration of one element and the geometric mean of the other components affecting dry matter production, giving a more complete indication of the nutritional balance, as evidenced by Schaller et al. (2002) and Parent et al. (2009). Employing the CND methodology for banana plants, Raghupathi et al. (2002) demonstrated the correlation between the norms and the nutrient concentrations in the Proc. 3 rd IS on Guava and Other Myrtaceae Eds.: C.A.F. Santos et al. Acta Hort. 959, ISHS

2 plant. García-Hernández et al. (2009), studying pecan trees in Mexico, identified four nutrient relations, one of them positive (N-R d ) and three negative (N-Ca, K-Mg and P-K), with the negative K-Mg and N-Ca relations being the most significant for discrimination between the high- and low-yield subpopulations. Nutritional diagnosis performed with adequate frequency generates a huge volume of data that requires computer processing to transform the data into useful information for decision making. The present article presents a program developed for this purpose specifically for guava, called CND-Goiaba 1.0. Its use is as a tool to perform the mathematical calculations to determine the CND indices for guava culture. MATERIAL AND METHODS The data for development and testing of the software were gathered in commercial plots (sample units) of guava trees located on farms in the municipalities of Taquaritinga, Monte Alto, Vista Alegre do Alto, Taiaçu, Pirangi and Monte Azul Paulista, all in the state of São Paulo. The region s climate is classified as Cwa according to the Köppen system, with a relatively dry winter and wet summer. The predominant soils in the orchard plots are Red- Yellow Argisol (Ultisol) and Red-Yellow Latosol (Oxisol). The study to develop and apply the software was conducted in 205 commercial plots (sample units), with guava trees (Psidium guajava Paluma ) ranging from 5 to 20 years in age, during the and growing seasons. The leaf sampling of the guava trees followed the recommendations of Natale et al. (1996) and Souza et al. (2010). The analytic measurements followed the recommendations of Bataglia et al. (1983). The software was developed in the C# (C Sharp) language and expresses the CND norms as proposed by Parent and Dafir (1992), Khiari et al. (2001) and Parent et al. (2005, 2009). RESULTS AND DISCUSSION Before formulating the CND norms, we computed the Gaussian distribution of the data, as described by Hair et al. (2005). The production variable was tested for normality by the Shapiro-Wilk test (W=0.988 with p=0.11) with acceptance of the Ho, i.e., the data were normally distributed. Of the 205 plots sampled, 69 (36.9%) presented yield greater than kg per plant (first production after the inflection point), making up the high-yield subpopulation, while 118 plots (63.1%) composed the low-yield subpopulation. The data from the 18 remaining plots were considered to be outliers according to the Mahalanobis distance (Parent et al., 2009), so they were excluded from the database. The nutritional composition diagnosis of the 187 valid commercial plots presented a mean inflection point of the cumulative function of kg per plant, a value that was taken as the base for determining the reference subpopulation, as shown in Figure 1. These data were thus employed to formulate the CND-Goiaba version 1.0 software. The values corresponding to the nutrient levels and mean productivity of the plots, divided between the high- and low-yield subpopulations (Fig. 1), are shown in Table 1. The minimum, maximum and average concentrations of the nutrients evaluated in the high- and low-yield subpopulations (Table 1) did not show differences regarding their classification in the ranges considered adequate by Natale et al. (1996), indicating the need to implement multivariate methods to diagnose the nutritional state. For the multi-nutrient variables, formulated according to Parent and Dafir (1992), Khiari et al. (2001) and Parent et al. (2005, 2009), in the reference population there was 100% acceptance of the hypothesis of normal frequency distribution, thus constituting the CND norms and their means and standard deviations (Table 2). Considering the lower limit of interpretation suggested by Natale et al. (1996) for the Paluma guava tree, the orchards sampled suffer from severe deficiencies, 162

3 particularly of Mg and Zn (Table 3). However, considering the analysis of the CND norms carried out for the mean nutrient concentrations suggested as adequate for guava trees by the same authors, IMg and IZn are the nutrients that present the greatest excess imbalances. Therefore, it is possible that despite the indication of Natale et al. (1996) of a CND-r 2 =12.21, this may overestimate the leaf content of Mg and Zn. Baumgartner and Cabrita (2001), analyzing the nutritional state of citrus orchards in the same region as the present study, also observed deficiencies of Zn in 51% of the orchards evaluated. To integrate and carry out the comparative calculations between the databases defined by the cumulative function (Fig. 1) and the samples to be compared, we formulated the CND-Goiaba version 1.0 program, which has the interfaces presented in Figure 2. We can therefore infer that the employment of CND analysis in commercial guava orchards in the state of São Paulo revealed the need for better nutritional balance in 63% of the orchards studied. In particular, the CND analysis showed severe nutritional imbalances of Mg and Zn. ACKNOWLEDGEMENT To FAPESP for financial support. Literature Cited Bataglia, O.C., Furlani, A.M.C., Teixeira, J.P.F., Furlani, P.R. and Gallo, J.R Método de análises química de plantas. Campinas: IAC. 48p. Baumgartner, J.G. and Cabrita, J.R.M Avaliação nutricional de pomares cítricos na região de Bebedouro, Jaboticabal: Funep, 2001, 24p. García-Hernández, J.L., Orona-Castillo, I., González-Cervantes, G., Valdez-Cepeda, R.D., Murillo-Amador, B., Troyo-Diéguez, E., Fortis-Hernández, M. and Segura- Castruita, M.A Interacciones nutrimentales y normas de diagnósitico de nutrimento compuesto en nogal pecanero (Carya illinoensis). Rev. Chapingo 15: Hair, J.F., Tatham, F., Ronald, L., Anderson, R.E., Rolph, E. and Black, W Análise multivariada de dados, 6 th ed. 688p. Khiari, L., Parent, L.E. and Tremblay, N Selecting the high-yield subpopulation for diagnosing nutrient imbalance in crops. Agronomy J. 93: Lagatu, H. and Maume, L. 1934a. Le diagsontic foliaire de la pomme de terre. Ann. Ecology Nature Agriculture 22: Lagatu, H. and Maume, L. 1934b. Recherches sur lê diagnostic foliaire. Ann. Ecology Nature Agriculture 22: LUPA Levantamento censitário de unidades de produção agrícola do Estado de São Paulo: SAA/CATI/IEA. Available at: Consulted on 13 December Natale, W., Coutinho, E.L.M., Boaretto, A.E. and Pereira, F.M Goiabeira: calagem e adubação. Jaboticabal: FUNEP. 22p. Parent, L.E., Khiari, L. and Pettigrew, A Nitrogen diagnosis of Christmas tree needle greenness. Can. J. Soil Sci. 85: Parent, L.E., Natale, W. and Ziadi, N Compositional nutrient diagnosis of corn using the Mahalanobis distance as nutrient imbalance index. Can. J. Soil Sci. 89: Parent, L.E. and Dafir, M A theorical concept of compositional nutriente diagnosis. J. Amer. Soc. Hort. Sci. 117: Raghupathi, H.B., Reddy, B.M.C. and Srinivas, K Multivariate diagnosis of nutrient imbalance in banana. Comm. Soil Sci. Plant Anal. 33: Rozane, D.E., Brugnara, V., Souza, H.A. and Amorim, D.A Condução, arquitetura e poda da goiabeira de mesa e/ou indústria. p In: W. Natale, D.E. Rozane, H.A. de Souza and D.A. Amorin (eds.), Cultura da goiaba do plantio a 163

4 comercialização. FCAV. FAPESP. CNPq, SBF 2. Schaller, K., Löhnertz, O. and Michel, H Modified Dris-System for leaf analysis to optimise fertilizer inputs further developments with grapevines. Acta Hort. 594: Souza, H.A., Rozane, D.E., Hernandes, A. and Natale, W Variação no teor de macronutrientes de folhas de goiabeira, em função do tipo e tempo de armazenamento. Biotemas. 23: Tables Table 1. Minimum, maximum and mean values and standard deviation (SD) for nutrient levels in guava leaves and yield (kg per plant), in samples collected during the and growing seasons, in the high- and low-yield subpopulations. Nutrient High yield Low yield Minimum Maximum Mean SD Minimum Maximum Mean SD N (g kg -1 ) P (g kg -1 ) K (g kg -1 ) Ca (g kg -1 ) Mg (g kg -1 ) S (g kg -1 ) B (mg kg -1 ) Cu (mg kg -1 ) Fe (mg kg -1 ) Mn (mg kg -1 ) Zn (mg kg -1 ) Yield Table 2. Upper limit, lower limit, standard deviation and distortion in guava leaves, in the high-yield subpopulation (1) in the and growing seasons. IN 1 IP IK ICa IMg IS IB ICu IFe IMn IZn CND-r 2 UL LL SD_m D Defined by the cumulative function, with n=69 plots. 2 Index. UL = upper limit, LL = lower limit, SD_m = mean standard deviation, D = distortion. 164

5 Table 3. Percentage of the limits of interpretation (LI) 1 of the nutrient concentrations of guava leaves collected in the and growing seasons (n=187). N P K Ca Mg S B Cu Fe Mn Zn Factor (%) Monte Alto n=34 LI Monte Azul n=15 LI Pirangi n=29 LI Taiaçu n=6 LI Taquaritinga n=4 LI Vista Alegre do Alto n=99 LI Percentage of samples below the lower limit of interpretation considered adequate by Natale et al. (1996) for the Paluma guava tree, for which the reference limits were: N<20.0, P<1.4, K<14.0, Ca<7.0, Mg<3.4, S<2.5, B<20, Cu<20, Fe<60, Mn<40 and Zn<25. Figures Fig. 1. Cumulative function from the guava database (n=187). 165

6 Fig. 2. Representation of the screen for interface of the CND-Goiaba version 1.0 program with hypothetical data. 166