Semina: Ciências Agrárias ISSN: 1676-546X [email protected] Universidade Estadual de Londrina Brasil
Cagliari Fioretto, Conrado; Tironi, Paulo; Pinto de Souza, José Roberto Growth and nutrient uptake patterns in plants of Duboisia sp Semina: Ciências Agrárias, vol. 37, núm. 4, julio-agosto, 2016, pp. 1883-1895 Universidade Estadual de Londrina Londrina, Brasil
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How to cite Complete issue Scientific Information System More information about this article Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Journal's homepage in redalyc.org Non-profit academic project, developed under the open access initiative DOI: 10.5433/1679-0359.2016v37n4p1883 Growth and nutrient uptake patterns in plants of Duboisia sp
Crescimento e marcha de absorção de nutrientes em plantas de Duboisia sp
Conrado Cagliari Fioretto1*; Paulo Tironi2; José Roberto Pinto de Souza3
Abstract
Characterizing growth and nutrient uptake is important for the establishment of plant cultivation techniques that aim at high levels of production. The culturing of Duboisia sp., although very important for world medicine, has been poorly studied in the field, since the cultivation of this plant is restricted to a few regions. The objective of this paper is to characterize growth and nutrient absorption during development in Duboisia sp. under a commercial cultivation system, and in particular to assess the distribution of dry matter and nutrients in the leaves and branches. Our work was performed on a commercial production farm located in Arapongas, Paraná, Brazil, from March 2009 to February 2010. A total of 10 evaluations took place at approximately 10-day intervals, starting 48 days after planting and ending at harvesting, 324 days after planting. The growth parameters analyzed were the height of the plants and the dry matter of the leaves and of branches, while the chemical composition of the leaves and branches was used to study nutrient absorption. Data were submitted to regression analysis. Growth in height followed Richards’ model, mirroring the rise in air temperature and water availability. Phytomass accumulation in the aerial parts of the plants was slow during the first 150 days, but grew 25 times from that point to harvesting. Starting from 260 days, phytomass accumulation in the leaves began to be notable, while in the branches, a growth of 169% occurred, a pattern of biomass partitioning that is unfavorable for the producer. Accumulation of nutrients mirrored the accumulation of phytomass, showing the same unfavorable partition. The nutritional demand for macronutrients and micronutrients was, respectively, N < K < Ca > P > Mg > S and Fe >Mn> Zn > B > Cu. Key words: Dry matter. Medicinal plants. Nutrition. Partition. Physiology.
Resumo
A caracterização do crescimento e da absorção de nutrientes de uma cultura é de grande importância para o estudo e o estabelecimento de técnicas de cultivo que visam à obtenção de elevado desempenho produtivo. A cultura da Duboisia sp., apesar de muito importante para a medicina mundial, tem seus aspectos da produção em campo pouco estudados, pois se trata de um cultivo restrito a poucas regiões. O objetivo deste trabalho foi caracterizar o crescimento e a marcha de absorção de nutrientes em plantas de Duboisia sp. sob condição comercial de cultivo, bem como a partição de matéria seca e nutrientes em folhas e ramos. O trabalho foi realizado em uma fazenda de produção comercial, localizada no município de Arapongas, PR, no período de março de 2009 a fevereiro de 2010. As avaliações em intervalos de aproximadamente 30 dias, iniciando-se aos 48 dias após o plantio e finalizando com a colheita, 324 dias após o plantio, totalizando 10 avaliações. Os parâmetros de crescimento analisados foram altura de planta, massa seca de folhas e massa seca de ramos, enquanto a composição química de folhas e ramos foi utilizada para estudar a absorção de nutrientes. Os dados foram submetidos à análise de regressão. O crescimento em altura ocorreu segundo o modelo de Richards, acompanhando a elevação da temperatura do ar e a disponibilidade hídrica. O acúmulo de fitomassa da parte aérea foi 1 Discente do Curso de Mestrado em Agronomia, Departamento de Agronomia, Universidade Estadual de Londrina, UEL, Londrina, PR, Brasil. E-mail: [email protected] 2 Engº Agrº, M.e, Solana Agropecuária Ltda, Arapongas, PR, Brasil. E-mail: [email protected] 3 Prof. Dr., Departamento de Agronomia, UEL, Londrina, PR, Brasil. E-mail: [email protected] * Author for correspondence Recebido para publicação 03/08/15 Aprovado em 02/06/16 1883 Semina: Ciências Agrárias, Londrina, v. 37, n. 4, p. 1883-1896, jul./ago. 2016 Fioretto, C. C.; Tironi, P.; Souza, J. R. P.
lento nos primeiros 150 dias e apresentou incremento de 25 vezes a partir desse ponto até a colheita. A partir dos 260 dias, o acúmulo de fitomassa de folhas passou a ser pouco significante, enquanto o de ramos teve um incremento de 169%, evidenciando uma partição de biomassa desfavorável ao produtor. O acúmulo de nutrientes acompanhou o acúmulo de fitomassa e, portanto, também mostrou a mesma partição desfavorável. A ordem de demanda nutricional para macronutrientes e micronutrientes foi, respectivamente, N>K>Ca>P>Mg>S e Fe>Mn>Zn>B>Cu. Palavras-chave: Fisiologia. Matéria seca. Nutrição. Partição. Plantas medicinais.
Introduction and times for planting and fertilizing. As a result, it will be possible to establish effective agronomical The genus Duboisia belongs to the Solanaceae practices, determine critical management times, family, which includes important species for predict the nutrient consumption and destination of world medicine, such as D. myoporoides and D. photosynthates, create strategies for the positioning leichhardtii, two species that naturally occur only of genotypes, estimate harvest time, and evaluate in Australia and New Caledonia. Duboisia plants new production systems (DUARTE et al., 2003; are perennial woody shrubs and can reach up to 14 GONDIM et al., 2011; MAUAD et al., 2013; meters in height. Their importance comes from the OLIVEIRA et al., 2000; ZABOT et al., 2004). tropane alkaloids in the leaves, which have been used primarily by the native peoples living in the Moreover, as the normal patterns of growth and plants’ original range, and also in war medicines, development for a plant are documented, easily and nowadays in anti-spasmodic and mydriatic measured parameters can be established that can drugs by the pharmaceutical industry (FOLEY, be practically evaluated during production, so as to 2006; OHLENDORF, 1996). follow the development of the plants and identify ongoing irregularities, a procedure that will create a Because laboratory production of the plants’ routine for continuous improvement. main active compounds is difficult, they are usually collected exclusively from field cultivation of the The objective of this study was to characterize plants (FOLEY, 2006). the accumulation of phytomass, the growth in plant height, the nutrient absorption rate, and the Because Duboisia is restricted to only a few biomass and nutrient partitioning among the leaves regions in the world, unlike cereals and oleaginous and branches in Duboisia sp. under commercial plants, few studies have been made of its field cultivation. management. Furthermore, because the supply of its active compounds to industry depends upon the success of agricultural production, it is Materials and Methods fundamental that an adequate production system be established. Understanding and characterizing the Our work was performed in a Duboisia ecophysiology of these plants is therefore critical commercial production area in Arapongas, Paraná, for the production process. Brazil (23°24′27″ S; 51°21′55″ E), between March 2009 and February 2010. According to the Köppen The study of phytomass accumulation, growth climatic classification KÖPPEN,( 1936), the region’s patterns, and nutrient absorption permits the climate is Cfa (humid temperate with hot summer), identification of periods of greater and lesser with an annual average temperature of 19.9°C and nutritional demand, as well as the distribution average annual precipitation of 1497 mm. Figure 1 of photosynthates among the organs of the plant shows the meteorological data on precipitation and according to its age and the time of the year, while air temperature collected between March 2009 and also making possible comparisons among genotypes, March 2010 by sensors located on the farm.
1884 Semina: Ciências Agrárias, Londrina, v. 37, n. 4, p. 1883-1896, jul./ago. 2016 Growth and nutrient uptake patterns in plants of Duboisia sp Figure 1. Precipitation (mm) and air temperature (ᵒC) during the experimental period. Arapongas, PR. Figure 1. Precipitation (mm) and air temperature (ᵒC) during the experimental period. Arapongas, PR.
The soil The was soil classified was classified as as dystrophicdystrophic oxisoil oxisoil (EMBRAPA,was chemically 2006) analyzedof a very clayeyaccording texture, to andEMBRAPA was (EMBRAPA,chemically 2006)analyzed of according a very clayeyto EMBRAPA texture, (2009). and Results(2009). of chemicalResults analysisof chemical are shown analysis in Table are 1.shown in Table 1. TableTable 1. Soil 1. testSoil resultstest results of the of experimental the experimental area area with with Duboisia Duboisia sp. plants.sp. plants. Arapongas, Arapongas, PR. PR. Depth pH OM* H+Al Ca2+ Mg2+ K2+ -3 -3 Depth---cm--- pH ---OMg dm* ------H+Al Cacmol2+ c dm ------Mg2+ K2+ 00 a 10 4,26 38,21-3 6,99 0,89 -30,49 0,78 ---cm------g dm ------cmolc dm ------00 a10 10 a 20 4,264,25 38,2133,49 6,846,99 1,100,89 0,400,49 0,45 0,78 10 a20 20 a 40 4,254,35 33,4920,75 5,896,84 1,061,10 0,400,40 0,21 0,45 20 a 40 4,35 20,75 5,89 1,06 0,40 0,21 Depth Extractable-P Rem-P ** Base Saturation Al3+ CEC -3 -3 -3 Depth---cm--- Extractable-P---mg dm ------Rem-Pg dm **--- Base------Saturation%------Al3+ --cmolc dmCEC-- 00 a 10 5,98-3 17,68-3 23,70 25,93 9,16 -3 ---cm------mg dm ------g dm ------%------cmolc dm -- 00 a10 10 a 20 5,984,72 17,6815,44 22,2523,70 30,2025,93 8,799,16 10 a20 20 a 40 4,721,45 15,4412,49 22,1122,25 26,6030,20 7,568,79 * ** 20Organic a 40 matter. Remaining1,45 phosphorus.12,49 Extractors: pH,22,11 CaCl2; OM, Walkley26,60-Black; Ca-Mg-K,7,56 KClIN; Extractable- P, Mehlich-1; Rem-P, Mehlich-1. * ** Organic matter. Remaining phosphorus. Extractors: pH, CaCl2; OM, Walkley-Black; Ca-Mg-K, KClIN; Extractable-P, Mehlich-1; Rem-P, Mehlich-1. 2 st The experimental area was 17600 m , with 5 m borders. Seedlings were planted on March 21 , 2009, at 3.50 × 1.30 m spacing, for a total of 3968 plants in the entire area and 3393 plants in the useful area. The experimentalEvaluations area were was made 17600 of both m dry2, with phytomass 5 height of leaves of andthe branchesplants, measured(DPL and DPB),10 different chemical times m borders.composition Seedlings of leaves were and planted branches, on andMarch also 21 thest, heightat intervals of the plants, of approximately measured 10 different30 days, times starting at 48 2009,intervals at 3.50 of × approximately 1.30 m spacing, 30 days, for startinga total of48 3968days after days planting after (48, planting 79, 109, (48, 140, 79, 201, 109, 232, 140, 262 201,, 293, 232, and 262, plants324 in daysthe entire after planting). area and Dry3393 phytom plantsass in ofthe the useful aerial parts293, (DPAP) and 324 was days calculated after planting).as the sum Dryof DPL phytomass and area. of the aerial parts (DPAP) was calculated as the DPB, and the relative height of the plants was calculated by treating the final height measurement as 100%. sum of DPL and DPB, and the relative height of EvaluationsThe were experimental made of design both used dry wasphytomass totally randomized, with 12 replications for plant height and dry the plants was calculated by treating the final height of leaves and branches (DPL and DPB), chemical measurement as 100%. composition of leaves and branches, and also the
1885 Semina: Ciências Agrárias, Londrina, v. 37, n. 4, p. 1883-1896, jul./ago. 2016 phytomass of leaves and branches, and three replications for chemical composition. Phytomass and chemical composition data were used to calibrate the growth curves and the rates of nutrient absorption, respectively. The soil was amended before planting, with the objective of achieving saturations of 65% Ca2+ and 15% Mg2+ (KINSEY; WALTERS, 2009), and 150% of relative phosphate (ALVAREZ et al., 2000), as well as neutralizing the toxic aluminum on the surface, so as to allow for the deepening of the root system, and increasing the bioavailability of micronutrients and sulfur. For that purpose, 3000 kg ha-1 of calcite limestone -1 (44%phytomass CaO, 4% of leavesMgO, 88%and branches, PRNT), 1100 and threekg ha replications of dolomitic for limestonechemical composition.(29% CaO, 18% Phytomass MgO, 86% and PRNT),chemical phytomass- 1of leaves and branches, and three replications for chemical-1 composition. Phytomass and chemical 1500composition kg ha of data agricu wereltural used gypsum to calibrate (17% the Ca, growth 14% S), curves 220 kg and ha the of rates thermal of nutrient phosphate absorption, (10% P 2respO5 sol.ectively. citric composition data were used-1 to calibrate the growth curves and the rates of nutrient absorption, respectively.2+ acid 2%), andThe 130 soil kg was ha amended of simple before superphosphate planting, with (18% the P objective2O5 sol. CAN+H of achieving2O) were saturations distributed of 65% by hauling, Ca and 2+ The soil was amended before planting, with the objective of achieving saturations of 65%-1 Ca and followed15% Mg by2+ (KINSEY;moldboard WALTERS, plowing to 2009),a depth and of 150% 20 cm, of relativeand then phosphate application (ALVAREZ of 2600 etkg al., ha 2000), of calcite as well 2+ 15% Mg (KINSEY;Fioretto, C. -C.;1WALTERS, Tironi, P.; Souza, 2009), J. R. and P. 150% of relative-1 phosphate (ALVAREZ et al., 2000),-1 as well limestone,as neutralizing 1200 kgthe ha toxic of aluminumdolomitic limestone,on the surface, 240 kgso haas to of allow thermal for phosphate,the deepenin andg of140 the kg root ha system, of simple and as neutralizing the toxic aluminum on the surface, so as to allow for the deepening of the-1 rootth system, and The experimental superphosphatedesignincreasing used the was bioavailability(18% totally P2O5 sol. of CAN+H Formicronutrients the2 evaluationO), and and finally of sulfur. the leveling nutrient For that harrowing. absorption purpose, rate,On3000 October kg ha 10of calcite, 2009, limestone 100 kg -1 randomized, with 12 replicationsincreasing-1 forthe bioavailabilityplant height ofsamples micronutrients of leaves and- 1and sulfur. branches For thatwere purpose, taken to 3000 the kg ha of calcite limestone ha(44% of ureaCaO, (45% 4% MgO,N) was 88% made PRNT), upon the1100 tree kg top ha projection. of dolomitic limestone (29% CaO, 18% MgO, 86% PRNT), -1 and dry phytomass of (44%leaves CaO, and 4%-1branches, MgO, 88%and PRNT),laboratory 1100 forkg chemicalha of dolomitic analysis limestoneof N,-1 P, K, (29% Ca, Mg, CaO, 18% MgO, 86% PRNT), 1500 kg Theha heightof agricu of lturalthe plants gypsum was (17% measured Ca, 14% with S), a 2202.50 kg m hawooden of thermal ruler, phosphatewhich was (10% positioned P2O5 sol.next citric to three replications for chemical-1 composition. S, B, Cu, Fe, Mn, and Zn, following-1 the methods 1500 kg ha of agricultural-1 gypsum (17% Ca, 14% S), 220 kg ha of thermal phosphate (10% P2O5 sol. citric theacid central 2%), axis and of130 the kg plant ha soof assimple to measure superphosphate the distance (18% between P2O5 sol.the soilCAN+H surface2O) and were the distributed tree top. by hauling, Phytomass and chemical composition data were-1 specified by EMBRAPA (2009). acid 2%), and 130 kg ha of simple superphosphate (18% P2O5 sol. CAN+H2O) were distributed by hauling, used to calibrate the growthfollowed curvesFor byand the moldboard the bio ratesmass of evaluation,plowing to thea depthaerial ofpart 20 was cm, collected and then by appliccuttingation the ofneck 2600 of thekg plantha-1 ofclose calcite to Data were submitted to a regression analysis by -1 nutrient absorption, respectively.followed by moldboard plowing to a depth of 20 cm, and then application of 2600 kg ha of calcite thelimestone, soil with 1200pruning kg hascissors.-1 of dolomitic CurveThe material Expert limestone, was 1.4 placed for240 Windowskg on ha a-1 shadingof (HYAMS,thermal screen phosphate, 1997), and desiccated and 140 at kg 65°C ha-1 fofor simple24 h -1 -1 -1 The soil was amendedlimestone, before 1200 planting, kg ha with of dolomiticso as to limestone, find the 240best kgmathematical ha of thermal model phosphate, for each and 140 kgth ha of simple insuperphosphate an industrial desiccator. (18% P2O After5 sol. drying, CAN+H branches2O), and and finally leaves leveling were manuallyharrowing. separated On October for weighing 10 , 2009, and 100 then kg the objective of achievingsuperphosphate saturations of (18% 65% PCaO2+ sol.variable. CAN+H TheO), eventual and finally choice leveling of the model harrowing. was made On October 10th, 2009, 100 kg milled-1 for chemical analysis.2 5 2 2+ ha of urea (45% N) was made upon the tree top projection. and 15% Mg (KINSEY;ha -1WALTERS, of urea (45% 2009), N) was and made based upon onthe thetree greatesttop projection. coefficient of determination 150% of relative phosphate (ALVAREZForThe the height evaluation et of al., the of plants (rthe2) nutrient and was minimalmeasured absorption pattern with rate, a error2.50 samples m of wooden estimation of leaves ruler, and (S), which branches was werepositioned taken tonext the to The height of the plants was measured with a 2.50 m wooden ruler, which was positioned next to 2000), as well as neutralizinglaboratorythe central the fortoxic axis chemical ofaluminum the plant analysis sotogether as ofto N,measure withP, K, thea Ca, satisfactory distance Mg, S, between B, explanation Cu, theFe, soilMn, for surface and the Zn, and following the tree to thep. methods the central axis of the plant so as to measure the distance between the soil surface and the tree top. on the surface, so as to specifiedallow for by theFor EMBRAPA deepening the biomass (2009).of evaluation,biological the behavior. aerial part was collected by cutting the neck of the plant close to the root system, and increasing theFor bioavailability the biomass evaluation, the aerial part was collected by cutting the neck of the plant close to the soil Datawith pruningwere submitted scissors. to The Thea regression material data of DPLwas analysis placedand DPB by on Curve were a shading splitExpert into screen 1.4 periods for and Windows desiccated (HYAMS, at 65°C f1997or 24), h of micronutrients and sulfur. For that purpose, 3000 the soil with pruning scissors. (upThe tomaterial 201 days was placed and after on a 201 shading days), screen to which and desiccated at 65°C for 24 h kg ha-1 of calcite limestonesoin as an (44% to industrial find CaO, the desiccator. 4%best MgO, mathematical After drying, model branches for each and variable. leaves wereThe eventualmanually choice separated of the for modelweighing was and made then in an industrial desiccator. Afterdifferent drying, models branches were and2 fitted. leaves wereFor the manually height ofseparated the for weighing and then 88% PRNT), 1100 kg habased-1 of ondolomitic the greatest limestone coefficient of determination (r ) and minimal pattern error of estimation (S), together milled for chemical analysis. plants (cm), relative height of the plants (%), the (29% CaO, 18% MgO, milled86% PRNT), for chemical 1500 analysis.kg ha-1 with a satisfactoryFor the explanationevaluation of DPAPfor the the nutrient (gbiological plant absorption-1), behavior. and the rate, DPL samples after 201of leaves days and (g branches were taken to the of agricultural gypsum (17% Ca,For 14% the S), evaluation 220 kg of the nutrient-1 absorption rate, samples of leaves and branches were taken to the The data of DPL andplant DPB), were the chosen split into model periods was Richards’,(up to 201 expressed days and after 201 days), to which ha-1 of thermal phosphate laboratory(10% P O forsol. chemical citric acid analysis of N, P, K, Ca, Mg, S, B, Cu, Fe, Mn, and Zn, following the methods laboratory2 for5 chemical analysisby Equationof N, P, 1,K, in Ca, which Mg, y S,is theB, Cu,analyzed Fe, Mn, variable, and Zn, following the methods 2%), and 130 kg ha-1 of simpledifferentspecified superphosphate models by EMBRAPA were (18%fitted. (2009). For the height of the plants (cm), relative height of the plants (%), the DPAP (g x is the age (days) and a, b, c, and d are coefficients P O5 sol. CAN+H O) werespecified distributed-1 by EMBRAPA by hauling, (2009). -1 2 2 plant ), andData the DPLwere aftersubmitted 201 todays tobe a estimated.(g regression plant ), t heanalysis chosen by model Curve was Expert Richards’, 1.4 for expressed Windows by (HYAMS, Equation 19971, in ), followed by moldboard plowing Datato a depthwere submittedof 20 to a regression analysis by Curve Expert 1.4 for Windows (HYAMS, 1997), whichso as y to is findthe analyzed the best variable,mathematical x is the model age (days) for each and variable. a, b, c, and The d areeventual coefficients choice to of be the estimated. model was made cm, and then application of 2600 kg ha-1 of calcite so as to find the best mathematical model for each variable.2 The eventual choice of the model was made -1 based on the greatest coefficient of determination (r )a and minimal pattern error of estimation (S), together limestone, 1200 kg ha of dolomitic limestone, 240 2 (1) (1) based on the greatest coefficient of determinationy (r ) and minimal1 pattern error of estimation (S), together kg ha-1 of thermal phosphate,with a andsatisfactory 140 kg explanationha-1 of for the biological behavior.1 ebcx d simple superphosphate (18%with aP satisfactoryO sol. CAN+H explanationO), for the biological behavior. 2 5 The data of 2DPL and DPB were split into periods (up to 201 days and after 201 days), to which and finally leveling harrowing.The Ondata October of DPLth 10 , and DPBThe werebest fitssplit for into DPL periods up to (up 201 to days 201 (gdays plant and-1) after 201 days), to which different models were fitted. For the height of the plantsbx (cm),-1 relative height of the plants (%), the DPAP (g -1 2009, 100 kg ha of urea different (45% N) models was made were uponfitted. Forand the DPB height up to of 201 the days yplants ax(g (cm),plant relative) were obtained height of by the plants (%), the(2) DPAP (g plant-1), and the DPL after 201 days (g plant-1), the chosen model was Richards’, expressed by Equation 1, in the tree top projection. -1 means of the Geometric-1 Model (Equation 2), while plant ), and the DPL after 201 days (g plant ), the chosen model-1 was Richards’, expressed by Equation 1, in which y is the analyzed variable,for xDPB is the after age 201(days) days and (ga, bplant, c, and), thed are Gaussian coefficients to be estimated. The height of the plants was measured with a 2 which y is the analyzed variable,Model x is thepresented age (days) the bestand (afit.b, xb), Inc, andthese d areequations, coefficients as to be estimated. 2.50 m wooden ruler, which was positioned next to 2ac2 y ae (3) in Equation 1, y isy the analyzeda variable,1 x is the age (1) the central axis of the plant so as to measure the bcx d (1) (days), and the othery symbols1 e are1 coefficients to be distance between the soil surface and the tree top. 1 ebcx d estimated. For the biomass evaluation, the aerial part was bx collected by cutting the neck of the plant close to the y ax (2) bx soil with pruning scissors. The material was placed y ax (2) (2)
on a shading screen and desiccated at 65°C for 24 (bx)2 h in an industrial desiccator. After drying, branches 22 y ae(b2xc) (3) (3) and leaves were manually separated for weighing y ae 2c2 (3) and then milled for chemical analysis. The model chosen for the absorption rates of
N, K, B, Cu, Fe, Mn, and Zn was the Gaussian
1886 Semina: Ciências Agrárias, Londrina, v. 37, n. 4, p. 1883-1896, jul./ago. 2016 (bx)2 y ae 2c2 (3)
The model chosen for the absorption rates of N, K, B, Cu, Fe, Mn, and Zn was the Gaussian (Equation 3). For P, Ca, and S, the best model was the Modified Exponential (Equation 4), while the model
identified as Vapor PressureGrowth (Equation and nutrient 5) was uptake the onepatterns chosen in plants for Mg. of Duboisia As in the sp other equations, y is the analyzed variable, x is the age (days), and the remaining symbols are coefficients to be estimated. (Equation 3). For P, Ca, and S, the best model was Resultsb and Discussion y ae x (4) the Modified Exponential (Equation 4), while the The growth in height of the Duboisia plants, based The model chosen for the absorption rates of N, K, B, Cu, Fe, Mn,b and Zn was the Gaussian model identified as Vapor Pressure (Equation 5) was a cln x The model chosen for the absorption rates of N, K, B, Cu, Fe,y Mn,e xandon age, Zn waswas thesatisfactorily Gaussian explained(5) by Richards’ (Equation 3). For P, Ca, andthe S, one the chosen best model for Mg. was As the in Modifiedthe other Exponentialequations, y (Equation 4), while the model Model (Figure 2) at a coefficient of determination (Equation 3). For P, Ca, andis S, the the analyzed best model variable, was thex is Modified the age (days), Exponential and the (Equation 4), while the model identified as Vapor Pressure (Equation 5) was the one chosen for Mg. As in the(r2 )other greater equations, than 0.99, y is the apparently mirroring the identified as Vapor Pressureremaining (Equation symbols 5)Because was are the the coefficients analyzedone chosen period tofor includedbe Mg. estimated. As the in complete the other cycle equations, from planting y is to thefirst harvest, the adjusted analyzed variable, x is the age (days), and the remaining symbols are coefficients riseto be inestimated. temperature and water availability, with models could not be extrapolated to other periods. analyzed variable, x is the age (days), and the remaining symbolsb are coefficients towhich be estimated. variables this growth exhibited correlation x y aeb (4) x coefficients of 0.88 and 0.43, respectively. The three y ae (4) (4) Results and Discussion first measurements were an exception: in them,
The growth in heightb of the Duboisia plants,growth based did on notage, appearwas satisfactorily to be considerably explained affectedby a cln x b x (5) by the2 lower temperatures and lower rainfall Richards’ Model (Figurey ea 2) catln xa coefficient of determination (r ) greater than(5) 0.99, apparently mirroring the y e x volumes. The mathematical(5) models that describe rise in temperature and water availability, with which variables this growth exhibited correlation coefficients the growth in height, with their respective estimated of 0.88 and 0.43, respectively. The three first measurements were an exception: in them, growth did not Because the analyzedBecause period includedthe analyzed the complete period cycle included from planting the coefficients,to first harvest, the the determination adjusted coefficient, and the Because the analyzed periodappear included to be considerably the complete affected cycle by thefrom lower planting temperatures to first and harvest, lower therainfall adjusted volumes. The mathematical models could not be extrapolatedcomplete to other cycle periods. from planting to first harvest, the pattern error of estimate, are presented in Table 2. models could not be extrapolatedadjusted modelsto other models that periods. describecould not the begrowth extrapolated in height, to with other their respective estimated coefficients, the determination
periods.coefficient, and the pattern error of estimate, are presented in Table 2.
Results and Discussion Results and Discussion The growth in heightFigureFigure 2.of Plant the 2. PlantheightDuboisia height of Duboisia ofplants, Duboisia sp. based as sp. a asfunctionon a functionage, of wasage of age(days). satisfactorily (days). Arapongas, Arapongas, explained PR. PR. by The growth in height of the Duboisia plants, based on age, was satisfactorily explained by Richards’ Model (Figure 2) at a coefficient of determination (r2) greater than 0.99, apparently mirroring the Richards’ Model (Figure 2) at a coefficient of determination (r2) greater than 0.99, apparently mirroring the rise in temperature and water availability, with which variables this growth exhibited correlation coefficients rise in temperature and water availability, with which variables this growth exhibited correlation coefficients of 0.88 and 0.43, respectively. The three first measurements were an exception: in them, growth did not of 0.88 and 0.43, respectively. The three first measurements were an exception: in them, growth did not appear to be considerably affected by the lower temperatures and lower rainfall volumes. The mathematical appear to be considerably affected by the lower temperatures and lower rainfall volumes. The mathematical models that describe the growth in height, with their respective estimated coefficients, the determination models that describe the growth in height, with their respective estimated coefficients, the determination coefficient, and the pattern error of estimate, are presented in Table 2. coefficient, and the pattern error of estimate, are presented in Table 2.
Figure 2. Plant height of Duboisia sp. as a function of age (days). Arapongas, PR. Figure 2. Plant height of Duboisia sp. as a function of age (days). Arapongas, PR.
Table 2. Coefficient of determination2 (r ), standard error of the estimate (S) and estimated coefficients of the mathematical models adjusted for height (cm) and relative height (%) of Duboisia sp. plants as a function of age (days). Arapongas, PR.
Parameter Model1 a b c d r2 S Height (cm) Richards 187,20 33,76 0,12 0,06 0,9970 3,7952 Relative Height (%) Richards 100,12 31,70 0,11 0,06 0,9974 1,9039
1 Nomenclature of software Curve Expert 1.4 for Windows (HYAMS,1997).
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Table 2. Coefficient of determination (r2), standard error of the estimate (S) and estimated coefficients of the Tablemathematical 2. Coefficient models of adjusted determination for height (r2), (cm)standard and errorrelative of theheight estimate (%) of (S) Duboisia and estimated sp. plants coefficients as a function of the of mathematical models adjusted for height (cm) and relative height (%) of Duboisia sp. plants as a function of
Table 2. Coefficient of determination (r2), standard error of the estimate (S) and estimated coefficients of the mathematical models adjusted for height (cm) and relative height (%) of Duboisia sp. plants as a function of age (days). Arapongas, PR.
Parameter Model1 a b c d r2 S Height (cm) Richards 187,20 33,76 0,12 0,06 0,9970 3,7952 Relative Height (%) Richards 100,12 31,70 0,11 0,06 0,9974 1,9039 1 Nomenclature of software Curve Expert 1.4 for Windows (HYAMS,1997). Fioretto, C. C.; Tironi, P.; Souza, J. R. P.
AccumulationAccumulation of DPAP of wasDPAP quite was slowquite slowup to up toapproach approximately of spring. 150 days In justafter 60planting days (Figure (between 3), day approximatelyprobably due 150 to low days temperatures after planting and (Figurelow rainfall 3), (Figure150 1). and Nevertheless, day 210), thefrom plants 150 days almost on, one tripled could their probablynotice due a significantto low temperatures accumulation, and matching low rainfall the temperature phytomass. and rainfall From 150increase days with up tothe theapproach last harvest, of (Figurespring. 1). In Nevertheless, just 60 days (between from 150 day days150 and on, day one 210), there the plants was almosta gain tripledof 25 theirtimes, phytomass. with 88% Fr ofom the 150 total coulddays notice up to a the significant last harvest, thereaccumulation, was a gain of matching 25 times, DPAP with 88% accumulation of the total DPAP occurring accumulation between occurri 200ng days the temperaturebetween 200 days and and rainfall the harvest. increase That increase with ledthe to aand concomitant the harvest. increase That in increasenutrient absorption led to a concomitant. increase in nutrient absorption.
FigureFigure 3. Dry 3. phytomassDry phytomass of aerial of aerial part (DPAP)part (DPAP) of Duboisia of Duboisia sp. as sp. a asfunction a function of age of age(days). (days).
In Figure 4, the DPAP accumulation curve leaves was nearly null. At 324 days, the branches In Figure 4, the DPAP accumulation curve was decomposed into DPL and DPB to highlight these was decomposed into DPL and DPB to highlight accounted for 77% of the DPAP, while the leaves th thesepartitions partitions within within the plant.the plant.From theFrom 200 the day 200 afterth plantingaccounted onward, for accumulation 23%. That occurred pattern predominantly of divergence day afterin branches planting rather onward, than leaves, accumulation so that in occurredthe last three was evaluations, also observed comprising by Bragança a period of et approxal. (2010)imately when predominantlythree months, in branchesbiomass gain rather in the than leaves leaves, was sonearly that null.studying At 324 days, the theaccumulation branches accounted and the for partitioning 77% of the of in theDPAP, last three while evaluations, the leaves accounted comprising for 23%. a period That ofpattern biomass of divergence in Conilon was also coffee. observed by Bragança et al.
approximately(2010) when three studying months, the accumulation biomass gain and thein partitioningthe of biomass in Conilon coffee.
Figure 4. Dry phytomass of leaves (DPL) and branches (DPB) of Duboisia sp. as a function of age (days). FigureArapongas, 4. Dry phytomass PR. of leaves (DPL) and branches (DPB) of Duboisia sp. as a function of age (days). Arapongas, PR.
1888 Semina: Ciências Agrárias, Londrina, v. 37, n. 4, p. 1883-1896, jul./ago. 2016 In contrast to coffee, in which the branches are expected to bear fruit, the target organ in Duboisia is the leaf, which is the source of the alkaloids of interest in medicine. The final three months of cultivation therefore represent unnecessary costs as well as a waste of resources, since the harvest could have been done at the 260th day after planting when DPL accumulation was stabilized. If that pattern repeats in subsequent years, a significant time reduction in the production cycle can be foreseen, greatly increasing the efficiency of soil, water, light, and nutrient use. Mathematical models adjusted for DPAP, DPL, and DPB and their respective estimated coefficients, coefficients of determination, and standard errors of the estimative are presented in Table 3.
Table 3. Coefficient of determination (r2), standard error of the estimate (S) and estimated coefficients of the mathematical models adjusted for dry phytomass of aerial part (DPAP), dry phytomass of leaves (DPL) and dry phytomass of branches (DPB) of Duboisia sp. as a function of age (days). Arapongas, PR.
Parameter Model1 a b C d r2 S DPAP Richards 1.251,41 28,87 0,10 4,32 0,9995 0,3703 DPL (up to 201 days) Geometric 2,04 0,003 - - 0,9938 2,2925 DPL (after 201 days) Richards 269,50 1.322,40 4,98 236,44 0,9870 20,9057 DPB (up to 201 days) Geometric 0,60 0,005 - - 0,9940 3,1018 DPB (after 201 days) Gaussian 963,50 324,99 47,54 - 0,9878 60,2659 1 Nomenclature of software Curve Expert 1.4 for Windows (HYAMS, 1997).
Figure 5 presents the rate of absorption of macronutrients (N, P, K, Ca, Mg, and S) and essential micronutrients (B, Cu, Fe, Mn, and Zn). The mathematical models adjusted for each nutrient, along with estimated coefficient, determination coefficient, and pattern error of the estimate, are presented in Table 4. The nutrient accumulation pattern was similar to the pattern for phytomass, showing that nutritional demand followed plant growth. Similar findings have been made by a number of authors such as Prado et al. (2011) Growth and nutrient uptake patterns in plants of Duboisia sp
In contrast to coffee, in which the branches are repeats in subsequent years, a significant time expected to bear fruit, the target organ in Duboisia reduction in the production cycle can be foreseen, is the leaf, which is the source of the alkaloids of greatly increasing the efficiency of soil, water, light, interest in medicine. The final three months of and nutrient use. Mathematical models adjusted for cultivation therefore represent unnecessary costs as DPAP, DPL, and DPB and their respective estimated well as a waste of resources, since the harvest could coefficients, coefficients of determination, and have been done at the 260th day after planting when standard errors of the estimative are presented in DPL accumulation was stabilized. If that pattern Table 3.
Table 3. Coefficient of determination2 (r ), standard error of the estimate (S) and estimated coefficients of the mathematical models adjusted for dry phytomass of aerial part (DPAP), dry phytomass of leaves (DPL) and dry phytomass of branches (DPB) of Duboisia sp. as a function of age (days). Arapongas, PR.
Parameter Model1 a b C d r2 S DPAP Richards 1.251,41 28,87 0,10 4,32 0,9995 0,3703 DPL (up to 201 days) Geometric 2,04 0,003 - - 0,9938 2,2925 DPL (after 201 days) Richards 269,50 1.322,40 4,98 236,44 0,9870 20,9057 DPB (up to 201 days) Geometric 0,60 0,005 - - 0,9940 3,1018 DPB (after 201 days) Gaussian 963,50 324,99 47,54 - 0,9878 60,2659
1 Nomenclature of software Curve Expert 1.4 for Windows (HYAMS, 1997).
Figure 5 presents the rate of absorption of et al. (2013) in crambe, and Pedrinho Júnior et al. macronutrients (N, P, K, Ca, Mg, and S) and (2004) in soy. Macronutrients and micronutrients essential micronutrients (B, Cu, Fe, Mn, and Zn). both came to be in high demand from the 200th day The mathematical models adjusted for each nutrient, after planting onward. The mathematical models along with estimated coefficient, determination that describe the accumulation of each nutrient, coefficient, and pattern error of the estimate, are their determination coefficients, coefficients of presented in Table 4. The nutrient accumulation determination and standard errors of the estimate pattern was similar to the pattern for phytomass, are presented in Table 4. Overall, the models showing that nutritional demand followed plant possess greater accuracy from the 170th day after growth. Similar findings have been made by a planting, and underestimate the phytomass during number of authors such as Prado et al. (2011) in the earlier period. The relative order of demand for tomato plants, Duarte et al. (2003) in corn, Mauad macronutrients and micronutrients, respectively, was N < K < Ca < P < Mg < S and Fe 1889 Semina: Ciências Agrárias, Londrina, v. 37, n. 4, p. 1883-1896, jul./ago. 2016 Fioretto, C. C.; Tironi, P.; Souza, J. R. P. Table 4. Coefficient of determination2 (r ), standard error of the estimate (S) and estimated coefficients of the mathematical models adjusted for the absorption rate of macronutrients (N, P, K, Ma, Mg and S) and micronutrients (B, Cu, Fe, Mn and Zn) in Duboisia sp. as a function age (days). Arapongas, PR. Parameter Mathematical Model1 r2 N y=25876,7482*exp((-((x-337,1493)^2)/10824,4718)) 0,9980 P y=48836,2616*exp(-1057,3055/x) 0,9782 K y=17211,1510*exp((-((x-364,0114)^2)/14615,0627)) 0,9979 Ca y=146650,5258*exp(-794,5195/x) 0,9619 Mg y=exp(-7,4651-188,7311/x+2,6615*ln(x)) 0,9958 S y=10157,5844*exp(-701,9546/x) 0,9598 B y=exp(83,7092-3499,9307/x-10,8705*ln(x)) 0,9662 Cu y=21282,9847*exp((-((x-325,1322)^2)/7669,4113)) 0,9986 Fe y=602644,1867*exp((-((x-310,2383)^2)/4983,3339)) 0,9989 Mn y=89609,2216*exp((-((x-345,4158)^2)/15587,5996)) 0,9925 Zn y=26244,1245*exp((-((x-354,1529)^2)/14447,7221)) 0,9965 1 Nomenclature of software Curve Expert 1.4 for Windows (HYAMS, 1997). In parallel with the accumulation pattern or dilution. Content levels of P, B, and Cu had a of phytomass in leaves and branches, nutrient trend toward concentration, while K, Mg, Fe, Mn, accumulation was greater in the branches than in and Zn underwent a decrease along the cycle, the leaves from around the 250th day after planting, showing dilution. In branches, the content variation sometimes in a remarkable way (Figure 6). This of most nutrients exhibited the same trends as in the was the case with phosphate, potassium, calcium, leaves, except for Ca, B, and Fe, with the two first copper, and iron, which latter nutrient had only 4% presenting a decreasing trend, and the last one an remaining in the leaves versus 96% in the branches. increasing trend. This pattern enhances the waste of resources, and Decreasing nutrient contents indicate that the in particular of fertilizers in the last three months rate of accumulation of phytomass is greater than the of cultivation, when nutrients that could have been nutrient absorption rate, while increasing nutrient used in a second cycle for leaf production were content levels indicate that absorption occurs faster extracted from the soil and allocated to the branches than phytomass accumulation. Such variation of the plants, an organ of no commercial interest. can occur either due to soil supply or for natural Nutrients displayed some differences among physiological reasons, as happens with potassium in themselves with respect to their levels in the leaves corn, which has completed, by about 70 days after and branches due to age (Figure 7). Most of them planting, 90% of its potassium absorption, followed had a decreasing trend, while some rose and others almost exclusively by redistribution (EMBRAPA, oscillated around an average throughout the cycle, 2010). not presenting a clear trend toward concentration 1890 Semina: Ciências Agrárias, Londrina, v. 37, n. 4, p. 1883-1896, jul./ago. 2016 reasons, as happens with potassium in corn, which has completed, by about 70 days after planting, 90% of its Growth and nutrient uptake patterns in plants of Duboisia sp potassium absorption, followed almost exclusively by redistribution (EMBRAPA, 2010). FigureFigure 5. AccumulationAccumulation curves curves of macronutrientof macronutrient (N, P, (N,K, Ca, P, MgK, Ca,and S)Mg and and micronutrients S) and micronutrients (B, Cu, Fe, Mn (B, e Cu,Zn) in Fe,Duboisia Mn e Zn)sp. as in a Duboisiafunction of sp. age as (days). a function Arapongas, of age PR. (days). Arapongas, PR. Nitrogen Phosphorus Potassium 30.000 2.000 20.000 25.000 1.500 15.000 1 20.000 1 1 - - - 15.000 1.000 10.000 g plant 10.000 g plant 500 g plant 5.000 5.000 0 0 0 0 100 200 300 400 0 100 200 300 400 0 100 200 300 400 Age (days) Age (days) Age (days) Calcium Magnesium Sulphur 14.000 2.000 1.500 12.000 10.000 1.500 1 1 - - 1.000 1 8.000 - 1.000 6.000 g plant g g plant 500 4.000 500 g plant 2.000 0 0 0 0 100 200 300 400 0 100 200 300 400 0 100 200 300 400 Age (days) Age (days) Age (days) Boron Copper Iron 30.000 25.000 800.000 25.000 20.000 600.000 1 20.000 - 1 1 - 15.000 - 15.000 400.000 10.000 10.000 mg plant mg plant 200.000 5.000 5.000 mg plant 0 0 0 0 100 200 300 400 0 100 200 300 400 0 100 200 300 400 Age (days) Age (days) Age (days) Manganese Zinc 100.000 30.000 80.000 25.000 20.000 1 1 - - 60.000 15.000 40.000 10.000 mg plant mg plant 20.000 5.000 0 0 0 100 200 300 400 0 100 200 300 400 Age (days) Age (days) 1891 Semina: Ciências Agrárias, Londrina, v. 37, n. 4, p. 1883-1896, jul./ago. 2016 Fioretto, C. C.; Tironi, P.; Souza, J. R. P. Figure 6. Dry phytomass partition (percentage of phytomass in the aerial part) of leaves and branches in Duboisia sp. as a function of age (days). Arapongas, PR. Figure 6. Dry phytomass partition (percentage of phytomass in the aerial part) of leaves and branches in Duboisia sp. as a function of age (days). Arapongas, PR. Nitrogen Leaves Phosphorus Leaves Potassium Leaves 100% Branches 100% Branches 100% Branches 80% 80% 80% 60% 60% 60% 40% 40% 40% phytomass phytomass 20% phytomass 20% 20% Participation in aerial Participation in aerial 0% Participation in aerial 0% 0% 0 100 200 300 400 0 100 200 300 400 0 100 200 300 400 Idade (dias) Age (days) Age (days) Calcium Leaves Magnesium Leaves Sulphur Leaves 100% Branches 100% Branches 100% Branches 80% 80% 80% 60% 60% 60% 40% 40% 40% phytomass phytomass phytomass 20% 20% 20% Participation in aerial Participation in aerial 0% 0% Participation in aerial 0% 0 100 200 300 400 0 100 200 300 400 0 100 200 300 400 Age (days) Age (days) Age (days) Leaves Boron Leaves Copper Leaves Iron Branches 100% Branches 100% Branches 100% 80% 80% 80% 60% 60% 60% 40% 40% 40% phytomass phytomass 20% 20% phytomass 20% Participation in aerial Participation in aerial 0% 0% Participation in aerial 0% 0 100 200 300 400 0 100 200 300 400 0 100 200 300 400 Age (days) Age (days) Age (days) Manganese Leaves Zinc Leaves 100% Branches 100% Branches 80% 80% 60% 60% 40% 40% phytomass 20% phytomass 20% Participation in aerial 0% Participation in aerial 0% 0 100 200 300 400 0 100 200 300 400 Age (days) Age (days) 1892 Semina: Ciências Agrárias, Londrina, v. 37, n. 4, p. 1883-1896, jul./ago. 2016 Growth and nutrient uptake patterns in plants of Duboisia sp FigureFigure 7.7. Macronutrient (N, (N, P, P,K, K, Ca, Ca, Mg, Mg, and andS) and S) micronutrientand micronutrient (B, Cu, (B, Fe, Cu, Mn Fe, and Mn Zn) andcontents Zn) contentsin leaves andin leavesbranches and of branches Duboisia of sp. Duboisia as a function sp. asof agea function (days). Arapongas,of age (days). PR. Arapongas, PR. Nitrogen Leaves Phosphorus Leaves Potassium Leaves 60 Branches 2,5 Branches 25 Branches ) ) 50 ) 1 20 - 2,0 1 1 - - 40 1,5 15 30 1,0 10 20 0,5 5 Content (g kg Content (g 10 kg Content (g Content (g kg Content (g 0 0,0 0 0 100 200 300 400 0 100 200 300 400 0 100 200 300 400 Age (days) Age (days) Age (days) Calcium Leaves Magnesium Leaves Sulphur Leaves 25 Branches 3,5 Branches 3,5 Branches 3,0 3,0 ) ) 20 ) 1 - 1 1 - 2,5 - 2,5 15 2,0 2,0 10 1,5 1,5 1,0 1,0 5 Content (g kg (g Content 0,5 0,5 Content (g kg (g Content kg (g Content 0 0,0 0,0 0 100 200 300 400 0 100 200 300 400 0 100 200 300 400 Age (days) Age (days) Age (days) Boron Leaves Copper Leaves Iron Leaves 60 Branches 30 Branches 1.000 ) ) ) 50 1 1 25 - - 1 800 - 40 20 600 30 15 400 20 10 10 5 200 Content (mg kg (mg Content Content (mg kg 0 0 Content kg (mg 0 0 100 200 300 400 0 100 200 300 400 0 100 200 300 400 Age (days) Age (days) Age (days) Manganese Leaves Zinc Leaves 350,0 70,0 Branches ) 300,0 ) 60,0 1 1 - 250,0 - 50,0 200,0 40,0 150,0 30,0 100,0 20,0 50,0 10,0 Content kg (mg 0,0 Content (mg kg 0,0 0 100 200 300 400 0 100 200 300 400 Age (days) Age (days) When nutrient relocation occurs, and one nutrient is extracted from an organ and supplied to another, a decrease occurs in the source organ’s nutrient content, allowing for an increase in the receptor organ, with no alteration in the overall nutrient content in the plant, but this was not the case in the studied Duboisia plants, where the content levels of all the nutrients was always increasing. 1893 Semina: Ciências Agrárias, Londrina, v. 37, n. 4, p. 1883-1896, jul./ago. 2016 Fioretto, C. C.; Tironi, P.; Souza, J. R. P. When nutrient relocation occurs, and one nutrient cafeeiro Conilon. Revista Ceres, Viçosa, v. 57, n. 1, p. is extracted from an organ and supplied to another, 48-52, 2010. a decrease occurs in the source organ’s nutrient DUARTE, A. P.; KIEHL, J. C.; CAMARGO, M. A. content, allowing for an increase in the receptor F.; RECO, P. C. Acúmulo de matéria seca e nutrientes em cultivares de milho originárias de clima tropical e organ, with no alteration in the overall nutrient introduzidas de clima temperado. Revista Brasileira de content in the plant, but this was not the case in the Milho e Sorgo, Sete Lagoas, v. 2, n. 3, p. 1-20, 2003. studied Duboisia plants, where the content levels of EMPRESA BRASILEIRA DE PESQUISA all the nutrients was always increasing. AGROPECUÁRIA – EMBRAPA. Centro Nacional de Pesquisa Em Solo. Sistema brasileiro de classificação de solos. Brasília: Embrapa-SPI; Rio de Janeiro: Embrapa- Conclusions Solos, 2006. 306p. ______. 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Acúmulo de massa seca e macronutrientes por FILHO, O. A.; UHRY, D.; STEFANELO, C.; LOSEKAN, plantas de Glycine max e Richardia brasiliensis. Planta M. E.; FARIAS, J. R.; LUDWIG, M. P. Análise de Daninha, Viçosa, MG, v. 22, n. 1, p. 53-61, 2004. crescimento da cultivar de feijão BR IPAGRO 44 Guapo Brilhante cultivada na safrinha em quatro densidades de semeadura em Santa Maria-RS. Revista de Ciências Agroveterinárias, Lages, v. 3, n. 2, p. 105-115, 2004. 1895 Semina: Ciências Agrárias, Londrina, v. 37, n. 4, p. 1883-1896, jul./ago. 2016