Biological nitrogen fixation in soybean in Argentina

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Plant Soil DOI 10.1007/s11104-015-2459-8

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Biological nitrogen fixation in soybean in Argentina: relationships with crop, soil, and meteorological factors D. J. Collino & F. Salvagiotti & A. Perticari & C. Piccinetti & G. Ovando & S. Urquiaga & R. W. Racca

Received: 14 November 2014 / Accepted: 23 March 2015 # Springer International Publishing Switzerland 2015

Abstract Aims This study aims to (i) asses the contribution of biological N fixation (%Ndfa) in the soybean production area of Argentina, (ii) build a model for predicting %Ndfa using crop, soil, and meteorological variables, and (iii) estimate %Ndfa at the country level using values obtained in this study. Methods %Ndfa was assessed in paddocks and experimental plots located in an area 22–39° S and 56–66° W. %Ndfa was determined using the natural 15N abundance method. A complete data set of soil and meteorological variables (n–47) was used to develop a model for predicting %Ndfa.

Results A median value of %Ndfa in aboveground biomass of 60 % (interquartile range 46–71 %) was estimated. Larger %Ndfa values were observed in areas with high crops yields. When seed yield was above 3.7 Mg ha−1, effective rainfall during fallow and mean temperature in the seed-filling period explained %Ndfa. Below 3.7 Mg ha−1, soil phosphorus content, pH, and effective rainfall in the vegetative period explicated %Ndfa. Conclusions Soybean production systems in Argentina showed larger %Ndfa than reported values in literature that may affect global N balances. Identified soil and meteorological variables may be useful for predicting %Ndfa in future studies, taking into account their spatial variation in the soil-plant system.

Responsible Editor: Euan K. James. D. J. Collino : R. W. Racca Instituto de Fisiología y Recursos genéticos vegetales, INTA, Córdoba 5000 Cordoba, Argentina

Keywords Soybean . BNF . Nitrogen . Argentina

F. Salvagiotti (*) Agronomy Department, EEA Oliveros INTA, Ruta 11 km 353, C2206 Santa Fe, Argentina e-mail: [email protected]

Introduction

A. Perticari : C. Piccinetti Instituto de Microbiología y Zoología Agrícola, INTA, Las Cabañas y De los Reseros C.C. 25, 1712 Castelar, Argentina G. Ovando Climatología Agrícola, Facultad de Ciencias Agropecuarias, Universidad Nacional de Córdoba, 5000 Córdoba, Argentina S. Urquiaga Embrapa Agrobiologia, Caixa Postal 74505 CEP, 23851-970 Seropédica, DF, Brazil

Nitrogen supply is crucial for plant production because it is directly involved in the photosynthesis process (Kumar et al. 2004). Although unreactive atmospheric nitrogen (N2) is the largest N pool in the biosphere, making up ca. 79 % of dry air, crop production relies on the contribution of reactive N. This fraction presents a great variability in each particular agricultural system, depending on the balance between inputs (fertilizer N, mineralization from soil organic matter, cultivation of leguminous species that allow biological N

Plant Soil

fixation (BNF), biological fixation by free-living microorganisms, fixation by lightning, and other small gains) and outputs (harvested grain, soil erosion, leaching, and gaseous losses like denitrification or ammonia volatilization) (Robertson and Vitousek 2009). Soil N balance assessment is a useful tool for estimating the magnitude of N loss/gain when using legume crops, analyzing fertilizer use efficiency, or assessing the sustainability of N management in different agro-ecosystems. Building N budgets requires accurate estimations of inputs, especially those derived from BNF (Boyer et al. 2004). According to Smil (1999), BNF accounted for ca. 17 % of total N inputs in agriculture at a global scale. Similarly, Galloway et al. (2004) estimated that at global scale ca. 65 % earth’s inputs of reactive N come from BNF, from which cultivation-induced BNF may represent ca. 20 % of total anthropogenic N inputs. There are large uncertainties regarding the amounts of N derived from BNF in different agro-ecosystems, depending on the relative presence of grain crops, pulses, and pasture-fodder crops (Galloway et al. 2004). Estimating the contribution of BNF is crucial for analyzing nutrient use efficiency and human impact on the N cycle in agro-ecosystems in Latin America (Austin et al. 2013). In this region, soybean is the main crop providing BNF. In Brazil, soybean crop occupied ca. 31.3 million hectares in the 2014/2015 growing season (CONAB 2014). Studies conducted in Brazil by Alves et al. (2003) and Hungria et al. (2006) using different techniques to evaluate BNF (natural 15N abundance and N-ureide, respectively) reported that in wellmanaged fields, 70–85 % of the N required by soybeans is derived from the air, with N contributions in the order of 70 to 250 kg ha−1. In Argentina, soybean also dominates agricultural systems, occupying ca. 20 million hectares in the 2012/2013 growing season (SIIA-MAGyP 2014). The soybean production area in Argentina is located in an area 22–39° S and 56–66° W (Fig. 1), with annual average temperatures of 23– 14 °C from north to south, and annual average precipitation of 600–1100 mm from west to east (Alvarez and Lavado 1998; Hall et al. 1992). These climatic variations determine diverse seed yield and associated N uptake and, therefore, different BNF contributions. Studies of BNF in

Argentina are scarce and were mostly conducted in experimental plots with low seed yields. In these studies, BNF represented from 20 to 50 % of total N input (Alvarez et al. 1995; Di Ciocco et al. 2004, 2008, 2011). Preliminary studies in paddocks in the soybean production area in Argentina in the 2004/2005 growing season reported BNF values varying between 26 and 71 %, with an average of 44 % (Collino et al. 2007), but this information was not extensive. By contrast, Herridge et al. (2008) estimated a countryspecific plant N derived from N2 fixation of 80 % for Argentina. These authors assumed a relative contribution of BNF similar to that observed in Brazil (Alves et al. 2003; Hungria et al. 2006), arguing that soybean is mostly planted under no tillage in N-deficient soils. Herridge et al. (2008) did not take into account that soybean in Argentina is mainly cultivated in Mollisol soils, with organic matter content between 5.5 and 38 g kg−1 and pH between 5.5 and 7.5 in the upper 20-cm layer (Sainz Rozas et al. 2011). In Brazil, N-deficient tropical and sub-tropical soils have lower C content and lower pH (Hungria and Vargas 2000); hence, less potential N mineralization is expected than in soils from Argentina. BNF is highly sensitive to the presence of nitrates in the area surrounding nodules (Salvagiotti et al. 2009a, b; Vessey and Waterer 1992). Then, a lower contribution of BNF is expected in soybeanbased agricultural systems in Argentina than values observed in Brazil, affecting the regional N balance. Using a range of values from 20 to 50 % of N derived from BNF in seed, Austin et al. (2006) estimated negative N balances for soybean production systems in Argentina. Therefore, a more extensive and representative evaluation of BNF in soybean production systems in Argentina is needed to obtain more accurate estimations. Eighty kilograms of N in aboveground biomass are required for producing 1 Mg of soybean grain (Salvagiotti et al. 2008). However, if N contribution from roots and soil rhizodeposition is also included, this requirement will increase to ca. 107 kg N (Rochester et al. 1998; Salvagiotti et al. 2008). This N requirement is met mainly through soil and BNF. Nitrogen from starter fertilizers may also be a N source for soybean, but to a lesser extent. Contribution of BNF increases with decreasing soil

Plant Soil

nitrate concentration and/or fertilizer N rate, but it also depends on soybean productivity and environmental factors. Herridge et al. (2008) observed a close relationship between shoot growth and BNF, suggesting an increase in BNF contribution under suitable environmental conditions for increasing seed yield. Accordingly, environmental constraints to BNF include: temperature (Soares Novo et al. 1999), soil moisture (Serraj et al. 1999), soil pH (Parker and Harris 1977), P availability (Sa and Israel 1995), saline and sodic soil (Elsheikh 1998), and water logging (Bacanamwo and Purcell 1999). Therefore, a model for predicting BNF should include soil and environmental factors that affect both crop growth and BNF process. The main hypothesis of the present study is that BNF contribution in soybean in Argentina is lower than that reported for Brazil, affecting regional N balances. Likewise, a larger contribution of BNF to total N uptake in soybean is expected in areas where soil and environmental conditions may determine high seed yield. The objectives of this work were to: (i) asses the contribution of BNF in the soybean production area of Argentina, (ii) build a model for predicting BNF using crop, soil and meteorological variables, and (iii) estimate BNF at the country level using values obtained in the present study.

Materials and methods Experimental sites Biological N fixation was assessed in paddocks and experimental plots located between 22 and 39° S and 56 and 66° W (Fig. 1) from 2004/2005 to 2010/2011 growing seasons. The chosen fields had a long history of soybean cultivation and represented the range of soil and climate variation of this region. Soils included Vertisols (sites 17 and 23), Entisols (site 2), and Mollisols (rest of the sites), with C content from 6.4 to 45.4 g kg−1 and pH from 4.7 to 7 in the upper 20-cm soil (Table 1). Some of the experiments also explored different cultivars and planting dates in each location and year. In total, 86 cases were explored (Table 1). Each field followed recommended management strategies for cultivar selection, planting date, as well as weed, pest, and insect control.

Plant sampling and analysis Determination of biological N fixation Biological N fixation was determined using the natural 15 N abundance method (Shearer and Kohl 1986). Sorghum and/or glyphosate-resistant maize was planted in each paddock and served as reference plant for BNF estimation. Thus, there were micro-plots of reference plants adjacent to the soybean crop in each field. At peak crop dry matter (DM), i.e., mid- to late seed-filling period, between R6 and R7 (Fehr and Caviness 1977), two 1-m2 aboveground biomass samples of soybean and one 1-m2 sample of the reference plant were collected. All harvested material was dried in a forced air oven at 65 °C for 72 h. All samples were weighed and subsamples were ground in a Wiley mill. All plant samples were analyzed for total N content using a semi-micro Kjeldahl procedure (Nelson and Sommers 1973). Sub-samples used for N determination, containing approximately 35 mg N, were used for determining 15 N abundance using an automated continuous-flow isotope-ratio mass spectrometer consisting of a Finnigan DeltaPlus mass spectrometer coupled to the output of a Carlo Erba EA 1108 total C and N analyzer (Finnigan MAT, Bremen, Germany) at Embrapa Agrobiologia, Brazil. The proportion of N in soybean derived from the air (%Ndfa) was calculated as: %Ndfa ¼ 100

δ15 Nref −δ15 Nsoy δ15 Nref −B

ð1Þ

where δ15Nref and δ15Nsoy are the natural 15N abundance of the reference and soybean plants, respectively, and B is the 15N natural abundance of N in soybean that relies only on BNF. Total aboveground BNF-derived N (BNF, kg ha−1) was calculated from aboveground biomass of soybean, biomass N concentration, and %Ndfa. It is important to mention that the absolute values of this contribution may be underestimated since N in roots, nodules, and rhizodeposition are not included in the calculation. At peak biomass, this contribution may reach up to 40 % of total N uptake (Rochester et al. 1998). At harvest, seed yield (SY) was determined in each field by taking three to six samples of 2 to 5 m2 (depending on site) in the area where reference plants and soybean were planted. Seed yield was adjusted to a standard moisture content of 0.13 kg H2O kg grain−1. In four of the fields, seed yield was not determined.

Plant Soil Fig. 1 Distribution of paddocks and experimental plots used to determine biological nitrogen fixation. Horizontal lines correspond to parallels 30° and 36° S. Dotted red line represents the 800-mm isohyet

Determination of B value The value for B was estimated in a greenhouse experiment designed for testing different Bradyrhizobium japonicum strains; including E109 (the most widely used strain for commercial inoculants in Argentina recommended by the National Institute of Agricultural Technology (INTA)). Plants were grown in complete absence of mineral N using three replicates. Sterilized seeds inoculated with B. japonicum strains were planted in 15-L pots in sterilized vermiculite. Plants were irrigated with an N-free Hoagland’s nutrient solution during the crop cycle. Aerial biomass was sampled at R6, dried in an oven at 65 °C, ground, and analyzed for δ15N. B value for the E109 strain was −1.032‰, a value

closely below values reported in previous studies that evaluated B. japonicum. For different B. japonicum strains, Boddey et al. (2000) informed B values at R6 between −1.15 and −1.40‰. In another set of experiments, Pauferro et al. (2010) reported B values of 1.7‰ for shoot biomass at R6. Soil sampling and analysis In each field, composite soil samples (15–20 soil cores each sample) were taken at 20 cm depth before planting. Sampling was done at random, crossing out in each field in order to have a representative sample in the area where the reference plants and soybean were sampled for BNF determination.

Plant Soil Table 1 Soil properties in the upper 20-cm layer and δ 15 values in soybean and the reference crop in paddocks and experimental plots used to determine BNF Map ID

18 25 6 36 17 33 12 10 27 28 35 35 35 35 35 35 35 35 31 30 19 26 30 24 21 21 10 34 37 37 38 38 16 32 35 33 18 27 40 29 36 11 31 24 34 22

Location

Marcos Juarez Corral de Bustos La Virginia 9 de Julio C. del Uruguay Castelar Piquillín Jesus Maria Pergamino Ferre Gral Villegas Gral Villegas Gral Villegas Gral Villegas Gral Villegas Gral Villegas Gral Villegas Gral Villegas Lujan Vedia Oliveros Runciman Vedia Casilda C. Gomez C. Gomez Jesus Maria Huinca Renanco America 1 America 2 Catrilo 1 Catrilo 2 Las Parejas Junín Gral Villegas Castelar Marcos Juarez Pergamino Balcarce S.A. de Areco 9 de Julio Rafaela Lujan Casilda Huinca Renanco Los Surgentes

Growing seasona

2004–2005 2004–2005 2004–2005 2004–2005 2004–2005 2004–2005 2004–2005 2004–2005 2004–2005 2004–2005 2004–2005 2004–2005 2004–2005 2004–2005 2004–2005 2004–2005 2004–2005 2004–2005 2004–2005 2004–2005 2004–2005 2005–2006 2005–2006 2005–2006 2005–2006 2005–2006 2005–2006 2005–2006 2006–2007 2006–2007 2006–2007 2006–2007 2006–2007 2006–2007 2006–2007 2006–2007 2006–2007 2006–2007 2006–2007 2006–2007 2006–2007 2006–2007 2006–2007 2006–2007 2006–2007 2006–2007

δ15N values

Soil Soil type

Texture

Organic C (g kg−1)

pH

Soybean

Reference

TA TA EH TH AP VA TyHT TyHT TA TA TyH TyH TyH TyH TyH TyH TyH TyH TA TyH TA TyH TyH TA TA TA TyHT EH EnH EnH EH EH TA TyH TH VA TA TA TA TA TyH TA TA TA EH TA

SiL SiL SiL L LCl SiCl SiL L SiL SiL L L L L L L L L SiL L SiL SiL L SiL SiL SiL L SaL L L SaL SaL SiL SaL L SiCl SiL SiL SiL SiL L SiL SiL SiL SaL SiL

14.4 13.9 6.8 14.4 25.0 18.3 12.3 14.7 16.0 14.6 11.4 12.0 12.7 13.1 12.4 9.1 12.1 12.6 15.8 14.2 11.5 14.3 14.2 14.3 19.1 15.1 17.7 7.8 7.8 16.7 7.9 16.7 21.7 12.0 9.9 26.7 31.1 17.4 45.4 19.0 26.1 14.0 14.0 14.3 7.3 n.a.

5.6 5.7 5.8 5.8 6.4 5.9 6.3 6.4 5.4 5.8 5.9 5.7 5.9 6.0 6.0 6.0 5.9 5.9 5.3 5.1 5.5 5.4 5.1 6.1 5.9 5.5 6.5 6.3 6.4 6.5 7.0 6.7 6.1 6.1 5.8 5.7 6.3 5.5 5.1 5.5 6.0 5.7 5.6 6.1 6.7 n.a.

0.95 0.26 −0.35 2.27 8.41 0.78 2.01 4.68 2.79 0.96 −0.20 0.32 0.99 1.05 0.20 0.12 1.13 1.68 3.23 1.26 0.20 1.35 1.07 0.49 −0.48 0.47 −0.14 1.83 0.31 0.97 2.59 2.65 0.07 −0.58 0.74 5.06 1.54 1.11 1.55 1.20 2.40 3.44 3.03 −0.20 0.37 1.26

5.99 3.26 4.70 5.65 11.05 6.04 3.56 6.44 4.97 6.69 4.31 5.83 3.71 4.48 4.31 5.83 3.71 4.48 5.26 3.37 3.46 5.53 3.02 3.15 7.18 3.97 3.48 4.26 4.34 4.48 3.10 4.95 3.42 2.18 3.60 8.63 5.06 3.30 4.21 5.90 5.46 4.33 6.86 3.30 4.09 5.64

Plant Soil Table 1 (continued) Map ID

23 21 39 10 3 1 17 13 7 19 4 5 9 17 41 39 2 2 2 2 2 14 36 19 19 8 35 18 19 19 18 18 18 18 15 15 25 25 25 20

Location

Gualeguaychu C. Gomez Coronel Suarez Jesus Maria El Colorado J: V. González C. del Uruguay Rio I Famailla Oliveros La Ramada Tala Pozo Reconquista C. del Uruguay Barrow Coronel Suarez Tolloche 1 Tolloche 2 Tolloche 3 Tolloche 4 Tolloche 5 Parana 9 de Julio Oliveros 1 Oliveros 2 Bañado Ovanta Gral Villegas Marcos Juarez Oliveros 1 Oliveros 2 Marcos Juarez 1 Marcos Juarez 2 Marcos Juarez 3 Marcos Juarez 4 Tio Pujio 1 Tio Pujio 2 Corral de Bustos 1 Corral de Bustos 2 Corral de Bustos 3 Armstrong

Growing seasona

2006–2007 2006–2007 2006–2007 2006–2007 2007–2008 2007–2008 2006–2007 2006–2007 2006–2007 2006–2007 2009–2010 2009–2010 2009–2010 2009–2010 2009–2010 2009–2010 2009–2010 2009–2010 2009–2010 2009–2010 2009–2010 2009–2010 2009–2010 2009–2010 2009–2010 2009–2010 2009–2010 2009–2010 2010–2011 2010–2011 2010–2011 2010–2011 2010–2011 2010–2011 2010–2011 2010–2011 2010–2011 2010–2011 2010–2011 2010–2011

δ15N values

Soil Soil type

Texture

Organic C (g kg−1)

pH

Soybean

Reference

AP TA TA TyHT OH n.a. AP EH TH TA EH TyHT TA AP PP TA TU TU TU TU TU AA TyH TA TA TyHT TH TA TA TA TA TA TA TA Tab Tab TA TA TA TA

n.a. SiL SiL L SiL n.a. LCl SiL SiL SiL SiL SiL SiL LCl LCl SiL Cl Cl Cl Cl Cl SiL L SiL SiL L L SiL SiL SiL SiL SiL SiL SiL L L SiL SiL SiL SiL

21.4 22.5 21.5 10.6 21.5 7.1 26.1 n.a. 17.5 12.0 8.6 7.1 9.0 19.1 n.a. 21.5 6.4 6.4 6.4 6.4 6.4 n.a. 20.1 15.5 15.9 n.a. 12.8 n.a. 15.5 15.9 n.a. n.a. n.a. n.a. n.a. n.a. 13.9 13.9 13.9 15.0

6.3 5.6 6.1 6.5 4.7 6.7 6.4 n.a. 6.2 5.7 6.4 6.6 6.2 6.0 n.a. 6.1 6.8 6.8 6.8 6.8 6.8 n.a. 5.9 5.8 5.8 n.a. 6.0 n.a. 5.7 5.7 n.a. n.a. n.a. n.a. n.a. n.a. 5.7 5.7 5.7 5.8

1.94 −0.09 3.31 1.00 3.46 0.67 1.79 −0.36 1.15 −0.30 4.53 2.32 1.22 2.17 6.07 0.57 5.32 2.79 2.43 4.10 3.20 2.39 1.34 1.46 0.88 1.7 2.4 2.4 1.15 0.34 0.1 −0.20 −0.21 −0.39 0.60 0.60 −0.38 −0.18 −0.32 1.95

7.98 2.61 5.02 3.58 6.27 5.53 7.11 1.53 3.86 2.22 5.40 4.17 4.17 22.60 8.30 5.69 8.42 8.42 8.42 8.42 8.42 14.53 4.15 4.26 3.89 4.3 4.3 3.7 5.13 3.37 2.7 4.33 4.33 4.33 2.19 2.19 4.24 4.24 4.24 3.41

Soil type: Thaptoargic Hapludoll (TH), Typical Argiudoll (TA), Vertic Argiudoll (VA), Entic Haplustoll (EH), Argic Peludert (AP), Typical Hapludoll (TyH), Aquic Argiudoll (AA), Petrocalcic Paleudoll (PP), Typical Haplustoll (TyHT), Typical Ustifluvent (TU), Typical Argialboll (TAb), Entic Hapludoll (EnH) (SAGyP-INTA (1990)). Texture: loam (L), silty-loam (SiL), sandy-loam (SaL), loamy clay (LCl), silty-clay (SiCl), clay (Cl) n.a. not available data a

Planting dates ranged from 20th October to 11th January

Plant Soil

Carbon content (Walkley and Black 1934), phosphorus P-Bray I (Bray and Kurtz 1945), nitrate content (Bremner 1965), and pH (Reed and Cummings 1945) were determined. Carbon content was converted to organic matter (OM) using a recovery factor of 0.58. Meteorological variables and phenology Meteorological variables were calculated using meteorological and phenological data. Daily solar radiation and maximum and minimum temperature and rainfall were recorded. In each field, radiation and temperatures values were obtained from meteorological stations located at a distance of up to 50 km, whereas rainfall was recorded in each paddock. These meteorological values can be extrapolated to each field under study because the region is a flat plain. Full flowering (R2), start of seed-filling (R5), and physiological maturity (R7) stages (Fehr and Caviness 1977) were simulated using the SIFESOJA V 2010 phenological model (Peltzer and Peltzer 2010). This model simulates phenological stages of soybean using location, cultivar, and planting date as input variables. Meteorological variables were calculated for three sub-periods within the crop cycle: (i) vegetative: from planting to R2 (P–R2); (ii) reproductive: from R2 to R5 (R2–R5), and (iii) seed-filling: from R5 to R7 (R5–R7). For each sub-period, the following variables were calculated: (a) cumulative solar radiation (SR; MJ m−2); (b) mean air temperature (MT, °C); (c) cumulative reference evapotranspiration, using the Priestley–Taylor method as modified by Ritchie (1972) (ETP; mm); (d) cumulative effective rainfall (R; mm); and (e) cumulative effective rainfall in the fallow period (ERF; mm) . The fallow period began in April 1 (harvest of previous crop) and ended in the planting date. Cumulative effective rainfall in each sub-period was calculated following Dardanelli et al. (1992).

Central Pampas: between 30 and 36° S, and (iii) Southern Pampas: to 36° S (Fig. 1). Descriptive statistics were used for summarizing BNF, N uptake, and seed yield. The interquartile range (IQR; 25–75 % percentiles), which represents 50 % of all observations centered on the median, was used to describe each variable. A classification and regression tree (CART) algorithm was applied for %Ndfa as a response variable. Trees explain variation of a single-response variable by repeatedly splitting the data into more homogeneous groups, using combinations of the explanatory variables. The algorithm defines a threshold of the explanatory variable that splits data into groups that show homogeneity within them (De’ath and Fabricius 2000). A multiple linear regression procedure was performed for modeling %Ndfa as a function of crop, soil, and meteorological variables that showed low correlation among them, within each group previously defined by the CART algorithm. A stepwise variable selection procedure was run in order to identify significant variables (at the 0.05 probability level) that explain %Ndfa. Mallows’ Cp statistic and partial residuals were analyzed to identify appropriate models (Draper and Smith 1998).

Results Soil characteristics Soil types represent the main soils in the Argentinean soybean area. Soil texture in the upper 20 cm depth varied from sandy-loam (up to 85 % sand) in the west to clay soils (up to 51 % clay) in the east portions of the study area. At the same depth, soil P-Bray varied from 6 to 198 ppm, whereas soil pH ranged between 4.7 and 7 (Table 1). Carbon content showed values from 6.4 to 45.4 g kg−1 (representing 1.1 to 7.8 % of organic matter).

Data analysis

Meteorological variables

Data were analyzed as a whole and split, taking into account the major regions for soybean production in Argentina, as defined by the national multienvironment trials conducted annually throughout the INTA Soybean Network (Baigorri 1997). Three major regions are delimited by latitude: (i) North: to 30° S; (ii)

Meteorological variables showed a wide range of values, especially regarding precipitations and cumulative global solar radiation. Cumulative effective precipitations in the fallow period and during the crop season averaged 347 and 440 mm, respectively. On average, 39, 27, and 34 % of total effective precipitation occurred

Plant Soil

in the P–R2, R2–R5, and R5–R7 sub-periods, respectively (Table 2). Regarding cumulative radiation, of an average of 2495 MJ m−2 that occurred during the crop cycle, 46, 24, and 30 % occurred in the P–R2, R2–R5, and R5–R7 sub-periods, respectively (Table 2). Mean temperature showed lower variation, being 23, 23.4, and 21.3 °C, respectively (Table 2).

uptake is located in the roots at peak biomass (Rochester et al. 1998), the average contribution from BNF of the whole may be ca. 230 kg N ha−1. Crop biomass and fixed N were positively correlated (Fig. 3). The slope of the relationship indicated a crop requirement of ca. 19 kg of fixed N/1000 kg of aboveground biomass.

Seed yield, nitrogen uptake and biological N fixation

Relationship between BNF and crop, soil and meteorological variables

The analysis of the pooled data showed a seed yield range between 1.5 and 5.9 Mg ha−1, with an IQR of 2.9– 4.3 Mg ha−1 (Table 3). Average seed yield was higher in Central Pampas (3.7 Mg ha−1; IQR=3.3–4.4 Mg ha−1). In the north, average seed yield was 2.9 Mg ha−1 (IQR= 2.2–3.7 Mg ha−1). In the Southern Pampas region, seed yield averaged 2.2 Mg ha−1, with an IQR between 1.8 and 2.7 Mg ha−1, being the lowest average seed yield of all regions (Table 3). The number of cases in Southern Pampas was low (n=4), but this region is the one of lowest soybean area, representing 7 % of total harvested area in the country and contributing 6 % of national production. Shoot N uptake at R6/R7 averaged 256 kg N ha−1, with an IQR of 195–305 kg N ha−1 (Table 3). Mean N uptake values for each region showed the same trend as that observed for seed yield. Therefore, shoot N uptake was linearly related to seed yield, with a slope of 13.6 kg of grain/kg of N uptake. This value is similar to that observed in the meta-analysis performed by Salvagiotti et al. (2008), and all values fell within the dilution and accumulation curves suggested by those authors (Fig. 2). On average, the proportion of N derived from BNF in aboveground biomass accounted for 58 % of total N uptake (IQR between 46 and 71 %). However, a wide variation was observed, encompassing values between 12 and 90 % (Table 3). Among regions, Central pampas showed the highest values (ca. 60 %) and Southern Pampas showed the lowest ones (43 %) (Table 3), with the North region showing intermediate values (52 %). The analysis of the whole data, in absolute terms, showed an N derived from BNF in aboveground biomass ranging between 15 and 337 kg N ha−1, with an average value of 153 kg N ha−1 (98–197 kg N ha−1 as IQR) (Table 3). BNF was positively related to seed yield (r2 =0.55), with a slope of 52 kg N fixed/1000 kg of seed yield (Fig. 2). In order to at least demonstrate the relevance of belowground N, assuming that 40 % of total N

A complete data set comprising 43 paddocks, where all soil, crop, and meteorological variables were measured, was used for the CART analysis. CART algorithm showed that seed yield was the most important explanatory variable that split data into two groups with a threshold of 3.7 Mg ha−1. Two groups were then defined: (i) above 3.7 Mg ha−1 (SY>3.7) and (ii) below 3.7 Mg ha−1 (SY